[Significance] Big Models, or Foundation Models, have offered a new paradigm in smart agriculture. These models, built on the Transformer architecture, incorporate numerous parameters and have undergone extensive training, often showing excellent performance and adaptability, making them effective in addressing agricultural issues where data is limited. Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence, capable of processing diverse inputs, making informed decisions, and potentially overseeing entire farming systems autonomously. [Progress] The fundamental concepts and core technologies of big models are initially elaborated from five aspects: the generation and core principles of the Transformer architecture, scaling laws of extending big models, large-scale self-supervised learning, the general capabilities and adaptions of big models, and the emerging capabilities of big models. Subsequently, the possible application scenarios of the big model in the agricultural field are analyzed in detail, the development status of big models is described based on three types of the models: Large language models (LLMs), large vision models (LVMs), and large multi-modal models (LMMs). The progress of applying big models in agriculture is discussed, and the achievements are presented. [Conclusions and Prospects] The challenges and key tasks of applying big models technology in agriculture are analyzed. Firstly, the current datasets used for agricultural big models are somewhat limited, and the process of constructing these datasets can be both expensive and potentially problematic in terms of copyright issues. There is a call for creating more extensive, more openly accessible datasets to facilitate future advancements. Secondly, the complexity of big models, due to their extensive parameter counts, poses significant challenges in terms of training and deployment. However, there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency, thereby enhancing the performance of big models in agriculture. Thirdly, these advanced models demonstrate strong proficiency in analyzing image and text data, suggesting potential future applications in integrating real-time data from IoT devices and the Internet to make informed decisions, manage multi-modal data, and potentially operate machinery within autonomous agricultural systems. Finally, the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial. The public availability of these models is expected to refine their capabilities through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice, which could revolutionize agricultural practices.

[Objective] It is of great significance to improve the efficiency and accuracy of crop pest detection in complex natural environments, and to change the current reliance on expert manual identification in the agricultural production process. Targeting the problems of small target size, mimicry with crops, low detection accuracy, and slow algorithm reasoning speed in crop pest detection, a complex scene crop pest target detection algorithm named YOLOv8-Entend was proposed in this research. [Methods] Firstly, the GSConv was introduecd to enhance the model's receptive field, allowing for global feature aggregation. This mechanism enables feature aggregation at both node and global levels simultaneously, obtaining local features from neighboring nodes through neighbor sampling and aggregation operations, enhancing the model's receptive field and semantic understanding ability. Additionally, some Convs were replaced with lightweight Ghost Convolutions and HorBlock was utilized to capture longer-term feature dependencies. The recursive gate convolution employed gating mechanisms to remember and transmit previous information, capturing long-term correlations. Furthermore, Concat was replaced with BiFPN for richer feature fusion. The bidirectional fusion of depth features from top to bottom and from bottom to top enhances the transmission of feature information acrossed different network layers. Utilizing the VoVGSCSP module, feature maps of different scales were connected to create longer feature map vectors, increasing model diversity and enhancing small object detection. The convolutional block attention module (CBAM) attention mechanism was introduced to strengthen features of field pests and reduce background weights caused by complexity. Next, the Wise IoU dynamic non-monotonic focusing mechanism was implemented to evaluate the quality of anchor boxes using "outlier" instead of IoU. This mechanism also included a gradient gain allocation strategy, which reduced the competitiveness of high-quality anchor frames and minimizes harmful gradients from low-quality examples. This approach allowed WIoU to concentrate on anchor boxes of average quality, improving the network model's generalization ability and overall performance. Subsequently, the improved YOLOv8-Extend model was compared with the original YOLOv8 model, YOLOv5, YOLOv8-GSCONV, YOLOv8-BiFPN, and YOLOv8-CBAM to validate the accuracy and precision of model detection. Finally, the model was deployed on edge devices for inference verification to confirm its effectiveness in practical application scenarios. [Results and Discussions] The results indicated that the improved YOLOv8-Extend model achieved notable improvements in accuracy, recall, mAP@0.5, and mAP@0.5:0.95 evaluation indices. Specifically, there were increases of 2.6%, 3.6%, 2.4% and 7.2%, respectively, showcasing superior detection performance. YOLOv8-Extend and YOLOv8 run respectively on the edge computing device JETSON ORIN NX 16 GB and were accelerated by TensorRT, mAP@0.5 improved by 4.6%, FPS reached 57.6, meeting real-time detection requirements. The YOLOv8-Extend model demonstrated better adaptability in complex agricultural scenarios and exhibited clear advantages in detecting small pests and pests sharing similar growth environments in practical data collection. The accuracy in detecting challenging data saw a notable increased of 11.9%. Through algorithm refinement, the model showcased improved capability in extracting and focusing on features in crop pest target detection, addressing issues such as small targets, similar background textures, and challenging feature extraction. [Conclusions] The YOLOv8-Extend model introduced in this study significantly boosts detection accuracy and recognition rates while upholding high operational efficiency. It is suitable for deployment on edge terminal computing devices to facilitate real-time detection of crop pests, offering technological advancements and methodologies for the advancement of cost-effective terminal-based automatic pest recognition systems. This research can serve as a valuable resource and aid in the intelligent detection of other small targets, as well as in optimizing model structures.

[Objective] Currently, pig farming facilities mainly rely on manual counting for tracking slaughtered and stored pigs. This is not only time-consuming and labor-intensive, but also prone to counting errors due to pig movement and potential cheating. As breeding operations expand, the periodic live asset inventories put significant strain on human, material and financial resources. Although methods based on electronic ear tags can assist in pig counting, these ear tags are easy to break and fall off in group housing environments. Most of the existing methods for counting pigs based on computer vision require capturing images from a top-down perspective, necessitating the installation of cameras above each hogpen or even the use of drones, resulting in high installation and maintenance costs. To address the above challenges faced in the group pig counting task, a high-efficiency and low-cost pig counting method was proposed based on improved instance segmentation algorithm and WeChat public platform. [Methods] Firstly, a smartphone was used to collect pig image data in the area from a human view perspective, and each pig's outline in the image was annotated to establish a pig count dataset. The training set contains 606 images and the test set contains 65 images. Secondly, an efficient global attention module was proposed by improving convolutional block attention module (CBAM). The efficient global attention module first performed a dimension permutation operation on the input feature map to obtain the interaction between its channels and spatial dimensions. The permuted features were aggregated using global average pooling (GAP). One-dimensional convolution replaced the fully connected operation in CBAM, eliminating dimensionality reduction and significantly reducing the model's parameter number. This module was integrated into the YOLOv8 single-stage instance segmentation network to build the pig counting model YOLOv8x-Ours. By adding an efficient global attention module into each C2f layer of the YOLOv8 backbone network, the dimensional dependencies and feature information in the image could be extracted more effectively, thereby achieving high-accuracy pig counting. Lastly, with a focus on user experience and outreach, a pig counting WeChat mini program was developed based on the WeChat public platform and Django Web framework. The counting model was deployed to count pigs using images captured by smartphones. [Results and Discussions] Compared with existing methods of Mask R-CNN, YOLACT(Real-time Instance Segmentation), PolarMask, SOLO and YOLOv5x, the proposed pig counting model YOLOv8x-Ours exhibited superior performance in terms of accuracy and stability. Notably, YOLOv8x-Ours achieved the highest accuracy in counting, with errors of less than 2 and 3 pigs on the test set. Specifically, 93.8% of the total test images had counting errors of less than 3 pigs. Compared with the two-stage instance segmentation algorithm Mask R-CNN and the YOLOv8x model that applies the CBAM attention mechanism, YOLOv8x-Ours showed performance improvements of 7.6% and 3%, respectively. And due to the single-stage design and anchor-free architecture of the YOLOv8 model, the processing speed of a single image was only 64 ms, 1/8 of Mask R-CNN. By embedding the model into the WeChat mini program platform, pig counting was conducted using smartphone images. In cases where the model incorrectly detected pigs, users were given the option to click on the erroneous location in the result image to adjust the statistical outcomes, thereby enhancing the accuracy of pig counting. [Conclusions] The feasibility of deep learning technology in the task of pig counting was demonstrated. The proposed method eliminates the need for installing hardware equipment in the breeding area of the pig farm, enabling pig counting to be carried out effortlessly using just a smartphone. Users can promptly spot any errors in the counting results through image segmentation visualization and easily rectify any inaccuracies. This collaborative human-machine model not only reduces the need for extensive manpower but also guarantees the precision and user-friendliness of the counting outcomes.

[Objective] The fresh cut rose industry has shown a positive growth trend in recent years, demonstrating sustained development. Considering the current fresh cut roses grading process relies on simple manual grading, which results in low efficiency and accuracy, a new model named Flower-YOLOv8s was proposed for grading detection of fresh cut roses. [Methods] The flower head of a single rose against a uniform background was selected as the primary detection target. Subsequently, fresh cut roses were categorized into four distinct grades: A, B, C, and D. These grades were determined based on factors such as color, size, and freshness, ensuring a comprehensive and objective grading system. A novel dataset contenting 778 images was specifically tailored for rose fresh-cut flower grading and detection was constructed. This dataset served as the foundation for our subsequent experiments and analysis. To further enhance the performance of the YOLOv8s model, two cutting-edge attention convolutional block attention module (CBAM) and spatial attention module (SAM) were introduced separately for comparison experiments. These modules were seamlessly integrated into the backbone network of the YOLOv8s model to enhance its ability to focus on salient features and suppressing irrelevant information. Moreover, selecting and optimizing the SAM module by reducing the number of convolution kernels, incorporating a depth-separable convolution module and reducing the number of input channels to improve the module's efficiency and contribute to reducing the overall computational complexity of the model. The convolution layer (Conv) in the C2f module was replaced by the depth separable convolution (DWConv), and then combined with Optimized-SAM was introduced into the C2f structure, giving birth to the Flower-YOLOv8s model. Precision, recall and F₁ score were used as evaluation indicators. [Results and Discussions] Ablation results showed that the Flower-YOLOv8s model proposed in this study, namely YOLOv8s+DWConv+Optimized-SAM, the recall rate was 95.4%, which was 3.8% higher and the average accuracy, 0.2% higher than that of YOLOv8s with DWConv alone. When compared to the baseline model YOLOv8s, the Flower-YOLOv8s model exhibited a remarkable 2.1% increase in accuracy, reaching a peak of 97.4%. Furthermore, mAP was augmented by 0.7%, demonstrating the model's superior performance across various evaluation metrics. The effectiveness of adding Optimized-SAM was proved. From the overall experimental results, the number of parameters of Flower-YOLOv8s was reduced by 2.26 M compared with the baseline model YOLOv8s, and the reasoning time was also reduced from 15.6 to 5.7 ms. Therefore, the Flower-YOLOv8s model was superior to the baseline model in terms of accuracy rate, average accuracy, number of parameters, detection time and model size. The performances of Flower-YOLOv8s network were compared with other target detection algorithms of Fast-RCNN, Faster-RCNN and first-stage target detection models of SSD, YOLOv3, YOLOv5s and YOLOv8s to verify the superiority under the same condition and the same data set. The average precision values of the Flower-YOLOv8s model proposed in this study were 2.6%, 19.4%, 6.5%, 1.7%, 1.9% and 0.7% higher than those of Fast-RCNN, Faster-RCNN, SSD, YOLOv3, YOLOv5s and YOLOv8s, respectively. Compared with YOLOv8s with higher recall rate, Flower-YOLOv8s reduced model size, inference time and parameter number by 4.5 MB, 9.9 ms and 2.26 M, respectively. Notably, the Flower-YOLOv8s model achieved these improvements while simultaneously reducing model parameters and computational complexity. [Conclusions] The Flower-YOLOv8s model not only demonstrated superior detection accuracy but also exhibited a reduction in model parameters and computational complexity. This lightweight yet powerful model is highly suitable for real-time applications, making it a promising candidate for flower grading and detection tasks in the agricultural and horticultural industries.

[Objective] The accurate estimation of terraced field areas is crucial for addressing issues such as slope erosion control, water retention, soil conservation, and increasing food production. The use of high-resolution remote sensing imagery for terraced field information extraction holds significant importance in these aspects. However, as imaging sensor technologies continue to advance, traditional methods focusing on shallow features may no longer be sufficient for precise and efficient extraction in complex terrains and environments. Deep learning techniques offer a promising solution for accurately extracting terraced field areas from high-resolution remote sensing imagery. By utilizing these advanced algorithms, detailed terraced field characteristics with higher levels of automation can be better identified and analyzed. The aim of this research is to explore a proper deep learning algorithm for accurate terraced field area extraction in high-resolution remote sensing imagery. [Methods] Firstly, a terraced dataset was created using high-resolution remote sensing images captured by the Gaofen-6 satellite during fallow periods. The dataset construction process involved data preprocessing, sample annotation, sample cropping, and dataset partitioning with training set augmentation. To ensure a comprehensive representation of terraced field morphologies, 14 typical regions were selected as training areas based on the topographical distribution characteristics of Yuanyang county. To address misclassifications near image edges caused by limited contextual information, a sliding window approach with a size of 256 pixels and a stride of 192 pixels in each direction was utilized to vary the positions of terraced fields in the images. Additionally, geometric augmentation techniques were applied to both images and labels to enhance data diversity, resulting in a high-resolution terraced remote sensing dataset. Secondly, an improved DeepLab v3+ model was proposed. In the encoder section, a lightweight MobileNet v2 was utilized instead of Xception as the backbone network for the semantic segmentation model. Two shallow features from the 4th and 7th layers of the MobileNet v2 network were extracted to capture relevant information. To address the need for local details and global context simultaneously, the multi-scale feature fusion (MSFF) module was employed to replace the atrous spatial pyramid pooling (ASPP) module. The MSFF module utilized a series of dilated convolutions with increasing dilation rates to handle information loss. Furthermore, a coordinate attention mechanism was applied to both shallow and deep features to enhance the network's understanding of targets. This design aimed to lightweight the DeepLab v3+ model while maintaining segmentation accuracy, thus improving its efficiency for practical applications. [Results and Discussions] The research findings reveal the following key points: (1) The model trained using a combination of near-infrared, red, and green (NirRG) bands demonstrated the optimal overall performance, achieving precision, recall, F₁-Score, and intersection over union (IoU) values of 90.11%, 90.22%, 90.17% and 82.10%, respectively. The classification results indicated higher accuracy and fewer discrepancies, with an error in reference area of only 12 hm². (2) Spatial distribution patterns of terraced fields in Yuanyang county were identified through the deep learning model. The majority of terraced fields were found within the slope range of 8º to 25º, covering 84.97% of the total terraced area. Additionally, there was a noticeable concentration of terraced fields within the altitude range of 1 000 m to 2 000 m, accounting for 95.02% of the total terraced area. (3) A comparison with the original DeepLab v3+ network showed that the improved DeepLab v3+ model exhibited enhancements in terms of precision, recall, F₁-Score, and IoU by 4.62%, 2.61%, 3.81% and 2.81%, respectively. Furthermore, the improved DeepLab v3+ outperformed UNet and the original DeepLab v3+ in terms of parameter count and floating-point operations. Its parameter count was only 28.6% of UNet and 19.5% of the original DeepLab v3+, while the floating-point operations were only 1/5 of UNet and DeepLab v3+. This not only improved computational efficiency but also made the enhanced model more suitable for resource-limited or computationally less powerful environments. The lightweighting of the DeepLab v3+ network led to improvements in accuracy and speed. However, the slection of the NirGB band combination during fallow periods significantly impacted the model's generalization ability. [Conclusions] The research findings highlights the significant contribution of the near-infrared (NIR) band in enhancing the model's ability to learn terraced field features. Comparing different band combinations, it was evident that the NirRG combination resulted in the highest overall recognition performance and precision metrics for terraced fields. In contrast to PSPNet, UNet, and the original DeepLab v3+, the proposed model showcased superior accuracy and performance on the terraced field dataset. Noteworthy improvements were observed in the total parameter count, floating-point operations, and the Epoch that led to optimal model performance, outperforming UNet and DeepLab v3+. This study underscores the heightened accuracy of deep learning in identifying terraced fields from high-resolution remote sensing imagery, providing valuable insights for enhanced monitoring and management of terraced landscapes.

[Objective] Currently, the lack of computerized systems to monitor the quality of cabbage transplants is a notable shortcoming in the agricultural industry, where transplanting operations play a crucial role in determining the overall yield and quality of the crop. To address this problem, a lightweight and efficient algorithm was developed to monitor the status of cabbage transplants in a natural environment. [Methods] First, the cabbage image dataset was established, the cabbage images in the natural environment were collected, the collected image data were filtered and the transplanting status of the cabbage was set as normal seedling (upright and intact seedling), buried seedling (whose stems and leaves were buried by the soil) and exposed seedling (whose roots were exposed), and the dataset was manually categorized and labelled using a graphical image annotation tool (LabelImg) so that corresponding XML files could be generated. And the dataset was pre-processed with data enhancement methods such as flipping, cropping, blurring and random brightness mode to eliminate the scale and position differences between the cabbages in the test and training sets and to improve the imbalance of the data. Then, a cabbage transplantation state detection model based on YOLOv8s (You Only Look Once Version 8s) was designed. To address the problem that light and soil have a large influence on the identification of the transplantation state of cabbage in the natural environment, a multi-scale attention mechanism was embedded to increase the number of features in the model, and a multi-scale attention mechanism was embedded to increase the number of features in the model. Embedding the multi-scale attention mechanism to increase the algorithm's attention to the target region and improve the network's attention to target features at different scales, so as to improve the model's detection efficiency and target recognition accuracy, and reduce the leakage rate; by combining with deformable convolution, more useful target information was captured to improve the model's target recognition and convergence effect, and the model complexity increased by C3-layer convolution was reduced, which further reduced the model complexity. Due to the unsatisfactory localization effect of the algorithm, the focal extended intersection over union loss (Focal-EIoU Loss) was introduced to solve the problem of violent oscillation of the loss value caused by low-quality samples, and the influence weight of high-quality samples on the loss value was increased while the influence of low-quality samples was suppressed, so as to improve the convergence speed and localization accuracy of the algorithm. [Results and Discussions] Eventually, the algorithm was put through a stringent testing phase, yielding a remarkable recognition accuracy of 96.2% for the task of cabbage transplantation state. This was an improvement of 2.8% over the widely used YOLOv8s. Moreover, when benchmarked against other prominent target detection models, the algorithm emerged as a clear winner. It showcased a notable enhancement of 3% and 8.9% in detection performance compared to YOLOv3-tiny. Simultaneously, it also managed to achieve a 3.7% increase in the recall rate, a metric that measured the efficiency of the algorithm in identifying actual targets among false positives. On a comparative note, the algorithm outperformed YOLOv5 in terms of recall rate by 1.1%, 2% and 1.5%, respectively. When pitted against the robust faster region-based convolutional neural network (Faster R-CNN), the algorithm demonstrated a significant boost in recall rate by 20.8% and 11.4%, resulting in an overall improvement of 13%. A similar trend was observed when the algorithm was compared to the single shot multibox detector (SSD) model, with a notable 9.4% and 6.1% improvement in recall rate. The final experimental results show that when the enhanced model was compared with YOLOv7-tiny, the recognition accuracy was increased by 3%, and the recall rate was increased by 3.5%. These impressive results validated the superiority of the algorithm in terms of accuracy and localization ability within the target area. The algorithm effectively eliminates interferenced factors such as soil and background impurities, thereby enhancing its performance and making it an ideal choice for tasks such as cabbage transplantation state recognition. [Conclusions] The experimental results show that the proposed cabbage transplantation state detection method can meet the accuracy and real-time requirements for the identification of cabbage transplantation state, and the detection accuracy and localization accuracy of the improved model perform better when the target is smaller and there are weeds and other interferences in the background. Therefore, the method proposed in this study can improve the efficiency of cabbage transplantation quality measurement, reduce the time and labor, and improve the automation of field transplantation quality survey.

[Objective] In recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields. [Methods] A method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F₁ were used to evaluate the model accuracy in this study. Results and Discussion The research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F₁ of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F₁ compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F₁ of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F₁ of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F₁=0.70) and SCR severity classification model (OA=71.39%, Macro F₁=0.69) were lower than that of NCLB. [Conclusions] The aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture.

[Significance] Soil stands as the fundamental pillar of agricultural production, with its quality being intrinsically linked to the efficiency and sustainability of farming practices. Historically, the intensive cultivation and soil erosion have led to a marked deterioration in some arable lands, characterized by a sharp decrease in soil organic matter, diminished fertility, and a decline in soil's structural integrity and ecological functions. In the strategic framework of safeguarding national food security and advancing the frontiers of smart and precision agriculture, the march towards agricultural modernization continues apace, intensifying the imperative for meticulous soil quality management. Consequently, there is an urgent need for the rrapid acquisition of soil's physical and chemical parameters. Interdisciplinary scholars have delved into soil monitoring research, achieving notable advancements that promise to revolutionize the way we understand and manage soil resource. [Progress] Utilizing the the Web of Science platform, a comprehensive literature search was conducted on the topic of "soil," further refined with supplementary keywords such as "electrochemistry", "spectroscopy", "electromagnetic", "ground-penetrating radar", and "satellite". The resulting literature was screened, synthesized, and imported into the CiteSpace visualization tool. A keyword emergence map was yielded, which delineates the trajectory of research in soil physical and chemical parameter detection technology. Analysis of the keyword emergence map reveals a paradigm shift in the acquisition of soil physical and chemical parameters, transitioning from conventional indoor chemical and spectrometry analyses to outdoor, real-time detection methods. Notably, soil sensors integrated into drones and satellites have garnered considerable interest. Additionally, emerging monitoring technologies, including biosensing and terahertz spectroscopy, have made their mark in recent years. Drawing from this analysis, the prevailing technologies for soil physical and chemical parameter information acquisition in agricultural fields have been categorized and summarized. These include: 1. Rapid Laboratory Testing Techniques: Primarily hinged on electrochemical and spectrometry analysis, these methods offer the dual benefits of time and resource efficiency alongside high precision; 2. Rapid Near-Ground Sensing Techniques: Leveraging electromagnetic induction, ground-penetrating radar, and various spectral sensors (multispectral, hyperspectral, and thermal infrared), these techniques are characterized by their high detection accuracy and swift operation. 3. Satellite Remote Sensing Techniques: Employing direct inversion, indirect inversion, and combined analysis methods, these approaches are prized for their efficiency and extensive coverage. 4. Innovative Rapid Acquisition Technologies: Stemming from interdisciplinary research, these include biosensing, environmental magnetism, terahertz spectroscopy, and gamma spectroscopy, each offering novel avenues for soil parameter detection. An in-depth examination and synthesis of the principles, applications, merits, and limitations of each technology have been provided. Moreover, a forward-looking perspective on the future trajectory of soil physical and chemical parameter acquisition technology has been offered, taking into account current research trends and hotspots. [Conclusions and Prospects] Current advancements in the technology for rapaid acquiring soil physical and chemical parameters in agricultural fields have been commendable, yet certain challenges persist. The development of near-ground monitoring sensors is constrained by cost, and their reliability, adaptability, and specialization require enhancement to effectively contend with the intricate and varied conditions of farmland environments. Additionally, remote sensing inversion techniques are confronted with existing limitations in data acquisition, processing, and application. To further develop the soil physical and chemical parameter acquisition technology and foster the evolution of smart agriculture, future research could beneficially delve into the following four areas: Designing portable, intelligent, and cost-effective near-ground soil information acquisition systems and equipment to facilitate rapid on-site soil information detection; Enhancing the performance of low-altitude soil information acquisition platforms and refine the methods for data interpretation to ensure more accurate insights; Integrating multifactorial considerations to construct robust satellite remote sensing inversion models, leveraging diverse and open cloud computing platforms for in-depth data analysis and mining; Engaging in thorough research on the fusion of multi-source data in the acquisition of soil physical and chemical parameter information, developing soil information sensing algorithms and models with strong generalizability and high reliability to achieve rapaid, precise, and intelligent acquisition of soil parameters.

[Objective] The technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization. By organizing multiple agricultural machinery units into convoys, unified control and collaborative operations can be achieved. This not only enhances operational efficiency and reduces costs, but also minimizes human labor input, thereby maximizing the operational potential of agricultural machinery. In order to solve the problem of communication delay in cooperative control of multi-vehicle formation and its compensation strategy, the trajectory control method of multi-vehicle formation was proposed based on model predictive control (MPC) delay compensator. [Methods] The multi-vehicle formation cooperative control strategy was designed, which introduced the four-vehicle formation cooperative scenario in three lanes, and then introduced the design of the multi-vehicle formation cooperative control architecture, which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model, and laied down a sturdy foundation for solving the formation following problem later. The testing and optimization of automatic driving algorithms based on real vehicles need to invest too much time and economic costs, and were subject to the difficulties of laws and regulations, scene reproduction and safety, etc. Simulation platform testing could effectively solve the above question. For the agricultural automatic driving multi-machine formation scenarios, the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines. Based on the single-machine dynamics model of the agricultural machine, a delay compensation controller based on MPC was designed. Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization. Based on the above model, the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution. The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible. And because the operation range of the actuator was limited, the control increment and control volume were designed with corresponding constraints. Finally, the control increment constraints were solved based on the front wheel angle constraints, front wheel angle increments, and control volume constraints of the agricultural machine. [Results and Discussions] Carsim and MATLAB/Simulink could be effectively compatible, enabling joint simulation of software with external solvers. When the delay step size d=5 was applied with delay compensation, the MPC response was faster and smoother; the speed error curve responded faster and gradually stabilized to zero error without oscillations. Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle. In the case of a longer delay step size d =10, controllers without delay compensation showed more significant performance degradation. Even under higher delay conditions, MPC with delay compensation applied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error, avoiding oscillations. The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions. The simulation results validated the effectiveness of the proposed formation control algorithm, ensuring that multiple vehicles could successfully change lanes to form queues while maintaining specific distances and speeds. Furthermore, the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks, achieving stable longitudinal and lateral control. This confirmed the feasibility of the model predictive controller with delay compensation proposed. [Conclusions] At present, most of the multi-machine formation coordination is based on simulation platform for verification, which has the advantages of safety, economy, speed and other aspects, however, there's still a certain gap between the idealized model in the simulation platform and the real machine experiment. Therefore, multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations.

[Objective] To fully utilize and protect farmland and lay a solid foundation for the sustainable use of land, it is particularly important to obtain real-time and precise information regarding farmland area, distribution, and other factors. Leveraging remote sensing technology to obtain farmland data can meet the requirements of large-scale coverage and timeliness. However, the current research and application of deep learning methods in remote sensing for cultivated land identification still requires further improvement in terms of depth and accuracy. The objective of this study is to investigate the potential application of deep learning methods in remote sensing for identifying cultivated land in the hilly areas of Southwest China, to provide insights for enhancing agricultural land utilization and regulation, and for harmonizing the relationship between cultivated land and the economy and ecology. [Methods] Santai county, Mianyang city, Sichuan province, China (30°42'34"~31°26'35"N, 104°43'04"~105°18'13"E) was selected as the study area. High-resolution imagery from two scenes captured by the Gaofen-6 (GF-6) satellite served as the primary image data source. Additionally, 30-meter resolution DEM data from the United States National Aeronautics and Space Administration (NASA) in 2020 was utilized. A land cover data product, SinoLC-1, was also incorporated for comparative evaluation of the accuracy of various extraction methods' results. Four deep learning models, namely Unet, PSPNet, DeeplabV3+, and Unet++, were utilized for remote sensing land identification research in cultivated areas. The study also involved analyzing the identification accuracy of cultivated land in high-resolution satellite images by combining the results of the random forest (RF) algorithm along with the deep learning models. A validation dataset was constructed by randomly generating 1 000 vector validation points within the research area. Concurrently, Google Earth satellite images with a resolution of 0.3 m were used for manual visual interpretation to determine the land cover type of the pixels where the validation points are located. The identification results of each model were compared using a confusion matrix to compute five accuracy evaluation metrics: Overall accuracy (OA), intersection over union (IoU), mean intersection over union (MIoU), F₁-Score, and Kappa Coefficient to assess the cultivated land identification accuracy of different models and data products. [Results and Discussions] The deep learning models displayed significant advances in accuracy evaluation metrics, surpassing the performance of traditional machine learning approaches like RF and the latest land cover product, SinoLC-1 Landcover. Among the models assessed, the UNet++ model performed the best, its F₁-Score, IoU, MIoU, OA, and Kappa coefficient values were 0.92, 85.93%, 81.93%, 90.60%, and 0.80, respectively. DeeplabV3+, UNet, and PSPNet methods followed suit. These performance metrics underscored the superior accuracy of the UNet++ model in precisely identifying and segmenting cultivated land, with a remarkable increase in accuracy of nearly 20% than machine learning methods and 50% for land cover products. Four typical areas of town, water body, forest land and contiguous cultivated land were selected to visually compare the results of cultivated land identification results. It could be observed that the deep learning models generally exhibited consistent distribution patterns with the satellite imageries, accurately delineating the boundaries of cultivated land and demonstrating overall satisfactory performance. However, due to the complex features in remote sensing images, the deep learning models still encountered certain challenges of omission and misclassification in extracting cultivated land. Among them, the UNet++ model showed the closest overall extraction results to the ground truth and exhibited advantages in terms of completeness of cultivated land extraction, discrimination between cultivated land and other land classes, and boundary extraction compared to other models. Using the UNet++ model with the highest recognition accuracy, two types of images constructed with different features—solely spectral features and spectral combined with terrain features—were utilized for cultivated land extraction. Based on the three metrics of IoU, OA, and Kappa, the model incorporating both spectral and terrain features showed improvements of 0.98%, 1.10%, and 0.01% compared to the model using only spectral features. This indicated that fusing spectral and terrain features can achieve information complementarity, further enhancing the identification effectiveness of cultivated land. [Conclusions] This study focuses on the practicality and reliability of automatic cultivated land extraction using four different deep learning models, based on high-resolution satellite imagery from the GF-6 in Santai county in China. Based on the cultivated land extraction results in Santai county and the differences in network structures among the four deep learning models, it was found that the UNet++ model, based on UNet, can effectively improve the accuracy of cultivated land extraction by introducing the mechanism of skip connections. Overall, this study demonstrates the effectiveness and practical value of deep learning methods in obtaining accurate farmland information from high-resolution remote sensing imagery.

[Objective] There exist a high genetic correlation among various morphological characteristics of Mongolian horses. Utilizing advanced technology to obtain body structure parameters related to athletic performance could provide data support for breeding institutions to develop scientific breeding plans and establish the groundwork for further improvement of Mongolian horse breeds. However, traditional manual measurement methods are time-consuming, labor-intensive, and may cause certain stress responses in horses. Therefore, ensuring precise and effective measurement of Mongolian horse body dimensions is crucial for formulating early breeding plans. [Method] Video images of 50 adult Mongolian horses in the suitable breeding stage at the Inner Mongolia Agricultural University Horse Breeding Technical Center was first collected. Fifty images per horse were captured to construct the training and validation sets, resulting in a total of 2 500 high-definition RGB images of Mongolian horses, with an equal ratio of images depicting horses in motion and at rest. To ensure the model's robustness and considering issues such as angles, lighting, and image blurring during actual image capture, a series of enhancement algorithms were applied to the original dataset, expanding the Mongolian horse image dataset to 4 000 images. The YOLOv8n-pose was employed as the foundational keypoint detection model. Through the design of the C2f_DCN module, deformable convolution (DCNV2) was integrated into the C2f module of the Backbone network to enhance the model's adaptability to different horse poses in real-world scenes. Besides, an SA attention module was added to the Neck network to improve the model's focus on critical features. The original loss function was replaced with SCYLLA-IoU (SIoU) to prioritize major image regions, and a cosine annealing method was employed to dynamically adjust the learning rate during model training. The improved model was named DSS-YOLO (DCNv2-SA-SIoU-YOLO) network model. Additionally, a test set comprising 30 RGB-D images of mature Mongolian horses was selected for constructing body dimension measurement tasks. DSS-YOLO was used for keypoint detection of body dimensions. The 2D keypoint coordinates from RGB images were fused with corresponding depth values from depth images to obtain 3D keypoint coordinates, and Mongolian horse's point cloud information was transformed. Point cloud processing and analysis were performed using pass-through filtering, random sample consensus (RANSAC) shape fitting, statistical outlier filtering, and principal component analysis (PCA) coordinate system correction. Finally, body height, body oblique length, croup height, chest circumference, and croup circumference were automatically computed based on keypoint spatial coordinates. [Results and Discussion] The proposed DSS-YOLO model exhibited parameter and computational costs of 3.48 M and 9.1 G, respectively, with an average accuracy mAP_0.5:0.95 reaching 92.5%, and a d_DSS of 7.2 pixels. Compared to Hourglass, HRNet, and SimCC, mAP_0.5:0.95 increased by 3.6%, 2.8%, and 1.6%, respectively. By relying on keypoint coordinates for automatic calculation of body dimensions and suggesting the use of a mobile least squares curve fitting method to complete the horse's hip point cloud, experiments involving 30 Mongolian horses showed a mean average error (MAE) of 3.77 cm and mean relative error (MRE) of 2.29% in automatic measurements. [Conclusions] The results of this study showed that DSS-YOLO model combined with three-dimensional point cloud processing methods can achieve automatic measurement of Mongolian horse body dimensions with high accuracy. The proposed measurement method can also be extended to different breeds of horses, providing technical support for horse breeding plans and possessing practical application value.

[Objective] In recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases. [Methods] To address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health. [Results and Discussions] The improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality. [Conclusions] The multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods.

[Objective] The body size parameter of cattle is a key indicator reflecting the physical development of cattle, and is also a key factor in the cattle selection and breeding process. In order to solve the demand of measuring body size of beef cattle in the complex environment of large-scale beef cattle ranch, an image acquisition device and an automatic measurement algorithm of body size were designed. [Methods] Firstly, the walking channel of the beef cattle was established, and when the beef cattle entered the restraining device through the channel, the RGB and depth maps of the image on the right side of the beef cattle were acquired using the Inter RealSense D455 camera. Secondly, in order to avoid the influence of the complex environmental background, an improved instance segmentation network based on Mask2former was proposed, adding CBAM module and CA module, respectively, to improve the model's ability to extract key features from different perspectives, extracting the foreground contour from the 2D image of the cattle, partitioning the contour, and comparing it with other segmentation algorithms, and using curvature calculation and other mathematical methods to find the required body size measurement points. Thirdly, in the processing of 3D data, in order to solve the problem that the pixel point to be measured in the 2D RGB image was null when it was projected to the corresponding pixel coordinates in the depth-valued image, resulting in the inability to calculate the 3D coordinates of the point, a series of processing was performed on the point cloud data, and a suitable point cloud filtering and point cloud segmentation algorithm was selected to effectively retain the point cloud data of the region of the cattle's body to be measured, and then the depth map was 16. Then the depth map was filled with nulls in the field to retain the integrity of the point cloud in the cattle body region, so that the required measurement points could be found and the 2D data could be returned. Finally, an extraction algorithm was designed to combine 2D and 3D data to project the extracted 2D pixel points into a 3D point cloud, and the camera parameters were used to calculate the world coordinates of the projected points, thus automatically calculating the body measurements of the beef cattle. [Results and Discussions] Firstly, in the part of instance segmentation, compared with the classical Mask R-CNN and the recent instance segmentation networks PointRend and Queryinst, the improved network could extract higher precision and smoother foreground images of cattles in terms of segmentation accuracy and segmentation effect, no matter it was for the case of occlusion or for the case of multiple cattles. Secondly, in three-dimensional data processing, the method proposed in the study could effectively extract the three-dimensional data of the target area. Thirdly, the measurement error of body size was analysed, among the four body size measurement parameters, the smallest average relative error was the height of the cross section, which was due to the more prominent position of the cross section, and the different standing positions of the cattle have less influence on the position of the cross section, and the largest average relative error was the pipe circumference, which was due to the influence of the greater overlap of the two front legs, and the higher requirements for the standing position. Finally, automatic body measurements were carried out on 137 beef cattle in the ranch, and the automatic measurements of the four body measurements parameters were compared with the manual measurements, and the results showed that the average relative errors of body height, cross section height, body slant length, and tube girth were 4.32%, 3.71%, 5.58% and 6.25%, respectively, which met the needs of the ranch. The shortcomings were that fewer body-size parameters were measured, and the error of measuring circumference-type body-size parameters was relatively large. Later studies could use a multi-view approach to increase the number of body rule parameters to be measured and improve the accuracy of the parameters in the circumference category. [Conclusions] The article designed an automatic measurement method based on two-dimensional and three-dimensional contactless body measurements of beef cattle. Moreover, the innovatively proposed method of measuring tube girth has higher accuracy and better implementation compared with the current research on body measurements in beef cattle. The relative average errors of the four body tape parameters meet the needs of pasture measurements and provide theoretical and practical guidance for the automatic measurement of body tape in beef cattle.

[Significance] The mechanization, automation and intelligentization of agricultural equipment are key factors to improve operation efficiency, free up labor force and promote the sustainable development of agriculture. It is also the hot spot of research and development of agricultural machinery industry in the future. In China, hills and mountains serves as vital production bases for agricultural products, accounting for about 70% of the country's land area. In addition, these regions face various environmental factors such as steep slopes, narrow road, small plots, complex terrain and landforms, as well as harsh working environment. Moreover, there is a lack of reliable agricultural machinery support across various production stages, along with a shortage of theoretical frameworks to guide the research and development of agricultural machinery tailored to hilly and mountainous locales. [Progress] This article focuses on the research advances of tractor leveling and anti-overturning systems in hilly and mountainous areas, including tractor body, cab and seat leveling technology, tractor rear suspension and implement leveling slope adaptive technology, and research progress on tractor anti-overturning protection devices and warning technology. The vehicle body leveling mechanism can be roughly divided into five types based on its different working modes: parallel four bar, center of gravity adjustable, hydraulic differential high, folding and twisting waist, and omnidirectional leveling. These mechanisms aim to address the issue of vehicle tilting and easy overturning when traversing or working on sloping or rugged roads. By keeping the vehicle body posture horizontal or adjusting the center of gravity within a stable range, the overall driving safety of the vehicle can be improved to ensure the accuracy of operation. Leveling the driver's cab and seats can mitigate the lateral bumps experienced by the driver during rough or sloping operations, reducing driver fatigue and minimizing strain on the lumbar and cervical spine, thereby enhancing driving comfort. The adaptive technology of tractor rear suspension and implement leveling on slopes can ensure that the tractor maintains consistent horizontal contact with the ground in hilly and mountainous areas, avoiding changes in the posture of the suspended implement with the swing of the body or the driving path, which may affect the operation effect. The tractor rollover protection device and warning technology have garnered significant attention in recent years. Prioritizing driver safety, rollover warning system can alert the driver in advance of the dangerous state of the tractor, automatically adjust the vehicle before rollover, or automatically open the rollover protection device when it is about to rollover, and timely send accident reports to emergency contacts, thereby ensuring the safety of the driver to the greatest extent possible. [Conclusions and Prospects] The future development directions of hill and mountain tractor leveling, anti-overturning early warning, unmanned, automatic technology were looked forward: Structure optimization, high sensitivity, good stability of mountain tractor leveling system research; Study on copying system of agricultural machinery with good slope adaptability; Research on anti-rollover early warning technology of environment perception and automatic interference; Research on precision navigation technology, intelligent monitoring technology and remote scheduling and management technology of agricultural machinery; Theoretical study on longitudinal stability of sloping land. This review could provide reference for the research and development of high reliability and high safety mountain tractor in line with the complex working environment in hill and mountain areas.

[Objective] With the development of agricultural informatization, a large amount of information about agricultural diseases exists in the form of text. However, due to problems such as nested entities and confusion of entity types, traditional named entities recognition (NER) methods often face challenges of low accuracy when processing agricultural disease text. To address this issue, this study proposes a new agricultural disease NER method called RoFormer-PointerNet, which combines the RoFormer pre-trained model with the PointerNet baseline model. The aim of this method is to improve the accuracy of entity recognition in agricultural disease text, providing more accurate data support for intelligent analysis, early warning, and prevention of agricultural diseases. [Methods] This method first utilized the RoFormer pre-trained model to perform deep vectorization processing on the input agricultural disease text. This step was a crucial foundation for the subsequent entity extraction task. As an advanced natural language processing model, the RoFormer pre-trained model's unique rotational position embedding approach endowed it with powerful capabilities in capturing textual positional information. In agricultural disease text, due to the diversity of terminology and the existence of polysemy, traditional entity recognition methods often faced challenges in confusing entity types. However, through its unique positional embedding mechanism, the RoFormer model was able to incorporate more positional information into the vector representation, effectively enriching the feature information of words. This characteristic enabled the model to more accurately distinguish between different entity types in subsequent entity extraction tasks, reducing the possibility of type confusion. After completing the vectorization representation of the text, this study further emploied a pointer network for entity extraction. The pointer network was an advanced sequence labeling approach that utilizes head and tail pointers to annotate entities within sentences. This labeling method was more flexible compared to traditional sequence labeling methods as it was not restricted by fixed entity structures, enabling the accurate extraction of all types of entities within sentences, including complex entities with nested relationships. In agricultural disease text, entity extraction often faced the challenge of nesting, such as when multiple different entity types are nested within a single disease symptom description. By introducing the pointer network, this study effectively addressed this issue of entity nesting, improving the accuracy and completeness of entity extraction. [Results and Discussions] To validate the performance of the RoFormer-PointerNet method, this study constructed an agricultural disease dataset, which comprised 2 867 annotated corpora and a total of 10 282 entities, including eight entity types such as disease names, crop names, disease characteristics, pathogens, infected areas, disease factors, prevention and control methods, and disease stages. In comparative experiments with other pre-trained models such as Word2Vec, BERT, and RoBERTa, RoFormer-PointerNet demonstrated superiority in model precision, recall, and F₁-Score, achieving 87.49%, 85.76% and 86.62%, respectively. This result demonstrated the effectiveness of the RoFormer pre-trained model. Additionally, to verify the advantage of RoFormer-PointerNet in mitigating the issue of nested entities, this study compared it with the widely used bidirectional long short-term memory neural network (BiLSTM) and conditional random field (CRF) models combined with the RoFormer pre-trained model as decoding methods. RoFormer-PointerNet outperformed the RoFormer-BiLSTM, RoFormer-CRF, and RoFormer-BiLSTM-CRF models by 4.8%, 5.67% and 3.87%, respectively. The experimental results indicated that RoFormer-PointerNet significantly outperforms other models in entity recognition performance, confirming the effectiveness of the pointer network model in addressing nested entity issues. To validate the superiority of the RoFormer-PointerNet method in agricultural disease NER, a comparative experiment was conducted with eight mainstream NER models such as BiLSTM-CRF, BERT-BiLSTM-CRF, and W2NER. The experimental results showed that the RoFormer-PointerNet method achieved precision, recall, and F₁-Score of 87.49%, 85.76% and 86.62%, respectively in the agricultural disease dataset, reaching the optimal level among similar methods. This result further verified the superior performance of the RoFormer-PointerNet method in agricultural disease NER tasks. [Conclusions] The agricultural disease NER method RoFormer-PointerNet, proposed in this study and based on the RoFormer pre-trained model, demonstrates significant advantages in addressing issues such as nested entities and type confusion during the entity extraction process. This method effectively identifies entities in Chinese agricultural disease texts, enhancing the accuracy of entity recognition and providing robust data support for intelligent analysis, early warning, and prevention of agricultural diseases. This research outcome holds significant importance for promoting the development of agricultural informatization and intelligence.

[Objective] The widespread prevalence of sclerotinia disease poses a significant challenge to the cultivation and supply of oilseed rape, not only results in substantial yield losses and decreased oil content in infected plant seeds but also severely impacts crop productivity and quality, leading to significant economic losses. To solve the problems of complex operation, environmental pollution, sample destruction and low detection efficiency of traditional chemical detection methods, a Bi-directional Gate Recurrent Unit (Bi-GRU) model based on space-spectrum feature fusion was constructed to achieve hyperspectral images (HSIs) segmentation of oilseed rape sclerotinia infected area. [Methods] The spectral characteristics of sclerotinia disease from a spectral perspective was initially explored. Significantly varying spectral reflectance was notably observed around 550 nm and within the wavelength range of 750-1 000 nm at different locations on rapeseed leaves. As the severity of sclerotinia infection increased, the differences in reflectance at these wavelengths became more pronounced. Subsequently, a rapeseed leaf sclerotinia disease dataset comprising 400 HSIs was curated using an intelligent data annotation tool. This dataset was divided into three subsets: a training set with 280 HSIs, a validation set with 40 HSIs, and a test set with 80 HSIs. Expanding on this, a 7×7 pixel neighborhood was extracted as the spatial feature of the target pixel, incorporating both spatial and spectral features effectively. Leveraging the Bi-GRU model enabled simultaneous feature extraction at any point within the sequence data, eliminating the impact of the order of spatial-spectral data fusion on the model's performance. The model comprises four key components: an input layer, hidden layers, fully connected layers, and an output layer. The Bi-GRU model in this study consisted of two hidden layers, each housing 512 GRU neurons. The forward hidden layer computed sequence information at the current time step, while the backward hidden layer retrieves the sequence in reverse, incorporating reversed-order information. These two hidden layers were linked to a fully connected layer, providing both forward and reversed-order information to all neurons during training. The Bi-GRU model included two fully connected layers, each with 1 000 neurons, and an output layer with two neurons representing the healthy and diseased classes, respectively. [Results and Discussions] To thoroughly validate the comprehensive performance of the proposed Bi-GRU model and assess the effectiveness of the spatial-spectral information fusion mechanism, relevant comparative analysis experiments were conducted. These experiments primarily focused on five key parameters—ClassAP(1), ClassAP(2), mean average precision (mAP), mean intersection over union (mIoU), and Kappa coefficient—to provide a comprehensive evaluation of the Bi-GRU model's performance. The comprehensive performance analysis revealed that the Bi-GRU model, when compared to mainstream convolutional neural network (CNN) and long short-term memory (LSTM) models, demonstrated superior overall performance in detecting rapeseed sclerotinia disease. Notably, the proposed Bi-GRU model achieved an mAP of 93.7%, showcasing a 7.1% precision improvement over the CNN model. The bidirectional architecture, coupled with spatial-spectral fusion data, effectively enhanced detection accuracy. Furthermore, the study visually presented the segmentation results of sclerotinia disease-infected areas using CNN, Bi-LSTM, and Bi-GRU models. A comparison with the Ground-Truth data revealed that the Bi-GRU model outperformed the CNN and Bi-LSTM models in detecting sclerotinia disease at various infection stages. Additionally, the Dice coefficient was employed to comprehensively assess the actual detection performance of different models at early, middle, and late infection stages. The dice coefficients for the Bi-GRU model at these stages were 83.8%, 89.4% and 89.2%, respectively. While early infection detection accuracy was relatively lower, the spatial-spectral data fusion mechanism significantly enhanced the effectiveness of detecting early sclerotinia infections in oilseed rape. [Conclusions] This study introduces a Bi-GRU model that integrates spatial and spectral information to accurately and efficiently identify the infected areas of oilseed rape sclerotinia disease. This approach not only addresses the challenge of detecting early stages of sclerotinia infection but also establishes a basis for high-throughput non-destructive detection of the disease.

[Objective] The traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing. [Methods] Initially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments. [Results and Discussion] The adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC. [Conclusions] The experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

[Objective] In the retail market for agricultural products, consumers are increasingly concerned about the safety and health aspects of those products. Traceability of blockchain has emerged as a crucial solution to address these concerns. Essentially, a blockchain functions as a dynamic, distributed, and shared database. When implemented in the agricultural supply chain, it not only improves product transparency to attract more consumers but also raises concerns about consumer privacy disclosure. The level of consumer apprehension regarding privacy will directly influence their choice to purchase agricultural products traced through blockchain-traced. Moreover, retailers' choices to sell blockchain-traced produce are influenced by consumer privacy concerns. By analyzing the impact of blockchain technology on the competitive strategies, pricing, and decision-making among agricultural retailers, they can develop market competition strategies that suit their market conditions to bolster their competitiveness and optimize the agricultural supply chain to maximize overall benefits. [Methods] Based on Nash equilibrium and Stackelberg game theory, a market competition model was developed to analyze the interactions between existing and new agricultural product retailers. The competitive strategies adopted by agricultural product retailers were analyzed under four different options of whether two agricultural retailers sell blockchain agricultural products. It delved into product utility, optimal pricing, demand, and profitability for each retailer under these different scenarios. How consumer privacy concerns impact pricing and profits of two agricultural product retailers and the optimal response strategy choice of another retailer when the competitor made the decision choice first were also analyzed. This analysis aimed to guide agricultural product retailers in making strategic choices that would safeguard their profits and market positions. To address the cooperative game problem of agricultural product retailers in market competition, ensure that retailers could better cooperate in the game, blockchain smart contract technology was used. By encoding the process and outcomes of the Stackelberg game into smart contracts, retailers could input their specific variables and receive tailored strategy recommendations. Uploading game results onto the blockchain network ensured transparency and encouraged cooperative behavior among retailers. By using the characteristics of blockchain, the game results were uploaded to the blockchain network to regulate the cooperative behavior, to ensure the maximization of the overall interests of the supply chain. [Results and Discussions] The research highlighted the significant improvement in agricultural product quality transparency through blockchain traceability technology. However, concerns regarding consumer privacy arising from this traceability could directly impact the pricing, profitability and retailers' decisions to provide blockchain-traceable items. Furthermore, an analysis of the strategic balance between two agricultural product retailers revealed that in situations of low and high product information transparency, both retailers were inclined to simultaneously offer sell traceable products. In such a scenario, blockchain traceability technology enhanced the utility and profitability of retail agricultural products, leading consumers to prefer purchase these traceable products from retailers. In cases where privacy concerns and agricultural product information transparency were both moderate, the initial retailer was more likely to opt for blockchain-based traceable products. This was because consumers had higher trust in the initial retailer, enabling them to bear a higher cost associated with privacy concerns. Conversely, new retailers failed to gain a competitive advantage and eventually exit the market. When consumer privacy concerns exceeded a certain threshold, both competing agricultural retailers discovered that offering blockchain-based traceable products led to a decline in their profits. [Conclusions] When it comes to agricultural product quality and safety, incorporating blockchain technology in traceability significantly improves the transparency of quality-related information for agricultural products. However, it is important to recognize that the application of blockchain for agricultural product traceability is not universally suitable for all agricultural retailers. Retailers must evaluate their unique circumstances and make the most suitable decisions to enhance the effectiveness of agricultural products, drive sales demand, and increase profits. Within the competitive landscape of the agricultural product retail market, nurturing a positive collaborative relationship is essential to maximize mutual benefits and optimize the overall profitability of the agricultural product supply chain.

[Objective] Farmland consolidation for agricultural mechanization in hilly and mountainous areas can alter the landscape pattern, elevation, slope and microgeomorphology of cultivated land. It is of great significance to assess the ecological risk of cultivated land to provide data reference for the subsequent farmland consolidation for agricultural mechanization. This study aims to assess the ecological risk of cultivated land before and after farmland consolidation for agricultural mechanization in hilly and mountainous areas, and to explore the relationship between cultivated land ecological risk and cultivated land slope. [Methods] Twenty counties in Tongnan district of Chongqing city was selected as the assessment units. Based on the land use data in 2010 and 2020 as two periods, ArcGIS 10.8 and Excel software were used to calculate landscape pattern indices. The weights for each index were determined by entropy weight method, and an ecological risk assessment model was constructed, which was used to reveal the temporal and spatial change characteristics of ecological risk. Based on the principle of mathematical statistics, the correlation analysis between cultivated land ecological risk and cultivated land slope was carried out, which aimed to explore the relationship between cultivated land ecological risk and cultivated land slope. [Results and Discussions] Comparing to 2010, patch density (PD), division (D), fractal dimension (FD), and edge density (ED) of cultivated land all decreased in 2020, while meant Patch Size (MPS) increased, indicating an increase in the contiguity of cultivated land. The mean shape index (MSI) of cultivated land increased, indicating that the shape of cultivated land tended to be complicated. The landscape disturbance index (U) decreased from 0.97 to 0.94, indicating that the overall resistance to disturbances in cultivated land has increased. The landscape vulnerability index (V) increased from 2.96 to 3.20, indicating that the structure of cultivated land become more fragile. The ecological risk value of cultivated land decreased from 3.10 to 3.01, indicating the farmland consolidation for agricultural mechanization effectively improved the landscape pattern of cultivated land and enhanced the safety of the agricultural ecosystem. During the two periods, the ecological risk areas were primarily composed of low-risk and relatively low-risk zones. The area of low-risk zones increased by 6.44%, mainly expanding towards the northern part, while the area of relatively low-risk zones increased by 6.17%, primarily spreading towards the central-eastern and southeastern part. The area of moderate-risk zones increased by 24.4%, mainly extending towards the western and northwestern part, while the area of relatively high-risk zones decreased by 60.70%, with some new additions spreading towards the northeastern part. The area of high-risk zones increased by 16.30%, with some new additions extending towards the northwest part. Overall, the ecological safety zones of cultivated relatively increased. The cultivated land slope was primarily concentrated in the range of 2° to 25°. On the one hand, when the cultivated land slope was less than 15°, the proportion of the slope area was negatively correlated with the ecological risk value. On the other hand, when the slope was above 15°, the proportion of the slope area was positively correlated with the ecological risk value. In 2010, there was a highly significant correlation between the proportion of slope area and ecological risk value for cultivated land slope within the ranges of 5° to 8°, 15° to 25°, and above 25°, with corresponding correlation coefficients of 0.592, 0.609, and 0.849, respectively. In 2020, there was a highly significant correlation between the proportion of slope area and ecological risk value for cultivated land slope within the ranges of 2° to 5°, 5° to 8°, 15° to 25°, and above 25°, with corresponding correlation coefficients of 0.534, 0.667, 0.729, and 0.839, respectively. [Conclusions] The assessment of cultivated land ecological risk in Tongnan district of Chongqing city before and after the farmland consolidation for agricultural mechanization, as well as the analysis of the correlation between ecological risk and cultivated land slope, demonstrate that the farmland consolidation for agricultural mechanization can reduce cultivated land ecological risk, and the proportion of cultivated land slope can be an important basis for precision guidance in the farmland consolidation for agricultural mechanization. Considering the occurrence of moderate sheet erosion from a slope of 5° and intense erosion from a slope of 10° to 15°, and taking into account the reduction of ecological risk value and the actual topographic conditions, the subsequent farmland consolidation for agricultural mechanization in Tongnan district should focus on areas with cultivated land slope ranging from 5° to 8° and 15° to 25°.

[Objective] The dynamic prediction of carbon emission from cold chain distribution is an important basis for the accurate assessment of carbon emission and its green credit grade. Facing the fact that the carbon emission of vehicles is affected by multiple factors, such as road condition information, driving characteristics, refrigeration parameters, etc., a dynamic prediction model of carbon emission was proposed from refrigerated vehicles that integrates multi-source information. [Methods] The backbone feature extraction network, neck feature fusion network and loss function of YOLOv8s was firstly improved. The full-dimensional dynamic convolution was introduced into the backbone feature extraction network, and the multidimensional attention mechanism was introduced to capture the contextual key information to improve the model feature extraction capability. A progressive feature pyramid network was introduced into the feature extraction network, which reduced the loss of key information by fusing features layer by layer and improved the feature fusion efficiency. The road condition information recognition model based on improved YOLOv8s was constructed to characterize the road condition information in terms of the number of road vehicles and the percentage of pixel area. Pearson's correlation coefficient was used to compare and analyze the correlation between carbon emissions of refrigerated vehicles and different influencing factors, and to verify the necessity and criticality of the selection of input parameters of the carbon emission prediction model. Then the iTransformer temporal prediction model was improved, and the external attention mechanism was introduced to enhance the feature extraction ability of iTransformer model and reduce the computational complexity. The dynamic prediction model of carbon emission of refrigerated vehicles based on the improved iTransformer was constructed by taking the road condition information, driving characteristics (speed, acceleration), cargo weight, and refrigeration parameters (temperature, power) as inputs. Finally, the model was compared and analyzed with other models to verify the robustness of the road condition information and the prediction accuracy of the vehicle carbon emission dynamic prediction model, respectively. [Results and Discussions] The results of correlation analysis showed that the vehicle driving parameters were the main factor affecting the intensity of vehicle carbon emissions, with a correlation of 0.841. The second factor was cargo weight, with a correlation of 0.807, which had a strong positive correlation. Compared with the vehicle refrigeration parameters, the road condition information had a stronger correlation between vehicle carbon emissions, the correlation between refrigeration parameters and the vehicle carbon emissions impact factor were above 0.67. In order to further ensure the accuracy of the vehicle carbon emissions prediction model, The paper was selected as the input parameters for the carbon emissions prediction model. The improved YOLOv8s road information recognition model achieved 98.1%, 95.5%, and 98.4% in precision, recall, and average recognition accuracy, which were 1.2%, 3.7%, and 0.2% higher than YOLOv8s, respectively, with the number of parameters and the amount of computation being reduced by 12.5% and 31.4%, and the speed of detection being increased by 5.4%. This was due to the cross-dimensional feature learning through full-dimensional dynamic convolution, which fully captured the key information and improved the feature extraction capability of the model, and through the progressive feature pyramid network after fusing the information between different classes through gradual step-by-step fusion, which fully retained the important feature information and improved the recognition accuracy of the model. The predictive performance of the improved iTransformer carbon emission prediction model was better than other time series prediction models, and its prediction curve was closest to the real carbon emission curve with the best fitting effect. The introduction of the external attention mechanism significantly improved the prediction accuracy, and its MSE, MAE, RMSE and R² were 0.026 1 %VOL, 0.079 1 %VOL, 0.161 5 %VOL and 0.940 0, respectively, which were 0.4%, 15.3%, 8.7% and 1.3% lower, respectively, when compared with iTransformer. As the degree of road congestion increased, the prediction accuracy of the constructed carbon emission prediction model increased. [Conclusions] The carbon emission prediction model for cold chain distribution under multi-source information fusion proposed in this study can realize accurate prediction of carbon emission from refrigerated vehicles, provide theoretical basis for rationally formulating carbon emission reduction strategies and promoting the development of low-carbon cold chain distribution.

[Objective] The daylily, a perennial herb in the lily family, boasts a rich nutritional profile. Given its economic importance, enhancing its yield is a crucial objective. However, current research on daylily cultivation is limited, especially regarding three-dimensional dynamic growth simulation of daylily plants. In order to establish a technological foundation for improved cultivation management, growth dynamics prediction, and the development of plant variety types in daylily crops, this study introduces an innovative three-dimensional dynamic growth and yield simulation model for daylily plants. [Methods] The open-source GroIMP software platform was used to simulate and visualize three-dimensional scenes. With Datong daylily, the primary cultivated variety of daylily in the Datong area, as the research subject, a field experiment was conducted from March to September 2022, which covered the growth season of daylily. Through actual cultivation experiment measurements, morphological data and leaf photosynthetic physiological parameters of daylily leaves, flower stems, flower buds, and other organs were collected. The functional-structural plant model (FSPM) platform's three-dimensional modeling technology was employed to establish the Cloud Cover-based solar radiation models (CSRMs) and the Farquhar, von Camerer, and Berry model (FvCB model) suitable for daylily. Moreover, based on the source-sink relationship of daylily, the carbon allocation model of daylily photosynthetic products was developed. By using the β growth function, the growth simulation model of daylily organs was constructed, and the daily morphological data of daylily during the growth period were calculated, achieving the three-dimensional dynamic growth and yield simulation of daylily plants. Finally, the model was validated with measured data. [Results and Discussions] The coefficient of determination (R²) between the measured and simulated outdoor surface solar radiation was 0.87, accompanied by a Root Mean Squared Error (RMSE) of 28.52 W/m². For the simulated model of each organ of the daylily plant, the R² of the measured against the predicted values ranged from 0.896 to 0.984, with an RMSE varying between 1.4 and 17.7 cm. The R² of the average flower bud yield simulation was 0.880, accompanied by an RMSE of 0.5 g. The overall F-value spanned from 82.244 to 1 168.533, while the Sig. value was consistently below the 0.05 significance level, suggesting a robust fit and statistical significance for the aforementioned models. Subsequently, a thorough examination of the light interaction, temperature influences, and photosynthetic attributes of daylily leaves throughout their growth cycle was carried out. The findings revealed that leaf nutrition growth played a pivotal role in the early phase of daylily's growth, followed by the contribution of leaf and flower stem nutrition in the middle stage, and finally the growth of daylily flower buds, which is the crucial period for yield formation, in the later stages. Analyzing the photosynthetic traits of daylily leaves comprehensively, it was observed that the photosynthetic rate was relatively low in the early spring as the new leaves were initially emerging and reached a plateau during the summer. Considering real-world climate conditions, the actual net photosynthetic rate was marginally lower than the rate verified under optimal conditions, with the simulated net assimilation rate typically ranging from 2 to 4 μmol CO₂/(m²·s). [Conclusions] The three-dimensional dynamic growth model of daylily plants proposed in this study can faithfully articulate the growth laws and morphological traits of daylily plants across the three primary growth stages. This model not only illustrates the three-dimensional dynamic growth of daylily plants but also effectively mimics the yield data of daylily flower buds. The simulation outcomes concur with actual conditions, demonstrating a high level of reliability.

[Objective] Manual disinfection in large-scale sheep farm is laborious, time-consuming, and often results in incomplete coverage and inadequate disinfection. With the rapid development of the application of artificial intelligence and automation technology, the automatic navigation and spraying robot for livestock and poultry breeding, has become a research hotspot. To maintain shed hygiene and ensure sheep health, an automatic navigation and spraying robot was proposed for sheep sheds. [Methods] The automatic navigation and spraying robot was designed with a focus on three aspects: hardware, semantic segmentation model, and control algorithm. In terms of hardware, it consisted of a tracked chassis, cameras, and a collapsible spraying device. For the semantic segmentation model, enhancements were made to the lightweight semantic segmentation model ENet, including the addition of residual structures to prevent network degradation and the incorporation of a squeeze-and-excitation network (SENet) attention mechanism in the initialization module. This helped to capture global features when feature map resolution was high, addressing precision issues. The original 6-layer ENet network was reduced to 5 layers to balance the encoder and decoder. Drawing inspiration from dilated spatial pyramid pooling, a context convolution module (CCM) was introduced to improve scene understanding. A criss-cross attention (CCA) mechanism was adapted to acquire context global features of different scales without cascading, reducing information loss. This led to the development of a double attention enet (DAENet) semantic segmentation model was proposed to achieve real-time and accurate segmentation of sheep shed surfaces. Regarding control algorithms, a method was devised to address the robot's difficulty in controlling its direction at junctions. Lane recognition and lane center point identification algorithms were proposed to identify and mark navigation points during the robot's movement outside the sheep shed by simulating real roads. Two cameras were employed, and a camera switching algorithm was developed to enable seamless switching between them while also controlling the spraying device. Additionally, a novel offset and velocity calculation algorithm was proposed to control the speeds of the robot's left and right tracks, enabling control over the robot's movement, stopping, and turning. [Results and Discussions] The DAENet model achieved a mean intersection over union (mIoU) of 0.945 3 in image segmentation tasks, meeting the required segmentation accuracy. During testing of the camera switching algorithm, it was observed that the time taken for the complete transition from camera to spraying device action does not exceed 15 seconds when road conditions changed. Testing of the center point and offset calculation algorithm revealed that, when processing multiple frames of video streams, the algorithm averages 0.04 to 0.055 per frame, achieving frame rates of 20 to 24 frames per second, meeting real-time operational requirements. In field experiments conducted in sheep farm, the robot successfully completed automatic navigation and spraying tasks in two sheds without colliding with roadside troughs. The deviation from the road and lane centerlines did not exceed 0.3 meters. Operating at a travel speed of 0.2 m/s, the liquid in the medicine tank was adequate to complete the spraying tasks for two sheds. Additionally, the time taken for the complete transition from camera to spraying device action did not exceed 15 when road conditions changed. The robot maintained an average frame rate of 22.4 frames per second during operation, meeting the experimental requirements for accurate and real-time information processing. Observation indicated that the spraying coverage rate of the robot exceeds 90%, meeting the experimental coverage requirements. [Conclusions] The proposed automatic navigation and spraying robot, based on the DAENet semantic segmentation model and center point recognition algorithm, combined with hardware design and control algorithms, achieves comprehensive disinfection within sheep sheds while ensuring safety and real-time operation.

[Objective] Rice plays a crucial role in daily diet. The rice industry involves numerous links, from paddy planting to the consumer's table, and the integrity of the quality control data chain directly affects the credibility of rice quality control and traceability information. The process of rice traceability also faces security issues, such as the leakage of privacy information, which need immediate solutions. Additionally, the previous practice of uploading all information onto the blockchain leads to high storage costs and low system efficiency. To address these problems, this study proposed a differential privacy-enhanced blockchain-based quality control model for rice, providing new ideas and solutions to optimize the traditional quality regulation and traceability system. [Methods] By exploring technologies of blockchain, interplanetary file system (IPFS), and incorporating differential privacy techniques, a blockchain-based quality control model for rice with differential privacy enhancement was constructed. Firstly, the data transmission process was designed to cover the whole industry chain of rice, including cultivation, acquisition, processing, warehousing, and sales. Each module stored the relevant data and a unique number from the previous link, forming a reliable information chain and ensuring the continuity of the data chain for quality control. Secondly, to address the issue of large data volume and low efficiency of blockchain storage, the key quality control data of each link in the rice industry chain was stored in the IPFS. Subsequently, the hash value of the stored data was returned and recorded on the blockchain. Lastly, to enhance the traceability of the quality control model information, the sensitive information in the key quality control data related to the cultivation process was presented to users after undergoing differential privacy processing. Individual data was obfuscated to increase the credibility of the quality control information while also protecting the privacy of farmers' cultivation practices. Based on this model, a differential privacy-enhanced blockchain-based quality control system for rice was designed. [Results and Discussions] The architecture of the differential privacy-enhanced blockchain-based quality control system for rice consisted of the physical layer, transport layer, storage layer, service layer, and application layer. The physical layer included sensor devices and network infrastructure, ensuring data collection from all links of the industry chain. The transport layer handled data transmission and communication, securely uploading collected data to the cloud. The storage layer utilized a combination of traditional databases, IPFS, and blockchain to efficiently store and manage key data on and off the blockchain. The traditional database was used for the management and querying of structured data. IPFS stored the key quality control data in the whole industry chain, while blockchain was employed to store the hash values returned by IPFS. This integrated storage method improved system efficiency, ensured the continuity, reliability, and traceability of quality control data, and provided consumers with reliable information. The service layer was primarily responsible for handling business logic and providing functional services. The implementation of functions in the application layer relied heavily on the design of a series of interfaces within the service layer. Positioned at the top of the system architecture, the application layer was responsible for providing user-centric functionality and interfaces. This encompassed a range of applications such as web applications and mobile applications, aiming to present data and facilitate interactive features to fulfill the requirements of both consumers and businesses. Based on the conducted tests, the average time required for storing data in a single link of the whole industry chain within the system was 1.125 s. The average time consumed for information traceability query was recorded as 0.691 s. Compared to conventional rice quality regulation and traceability systems, the proposed system demonstrated a reduction of 6.64% in the storage time of single-link data and a decrease of 16.44% in the time required to perform information traceability query. [Conclusions] This study proposes a differential privacy-enhanced blockchain-based quality control model for rice. The model ensures the continuity of the quality control data chain by integrating the various links of the whole industry chain of rice. By combining blockchain with IPFS storage, the model addresses the challenges of large data volume and low efficiency of blockchain storage in traditional systems. Furthermore, the model incorporates differential privacy protection to enhance traceability while safeguarding the privacy of individual farmers. This study can provide reference for the design and improvement of rice quality regulation and traceability systems.

[Objective] In order to summarize exemplary cases of high-quality development in regional smart agriculture and contribute strategies for the sustainable advancement of the national smart agriculture cause, the spatiotemporal characteristics and key driving factors of smart farms in the Yangtze River Economic Belt were studied. [Methods] Based on data from 11 provinces (municipalities) spanning the years 2014 to 2023, a comprehensive analysis was conducted on the spatio-temporal differentiation characteristics of smart farms in the Yangtze River Economic Belt using methods such as kernel density analysis, spatial auto-correlation analysis, and standard deviation ellipse. Including the overall spatial clustering characteristics, high-value or low-value clustering phenomena, centroid characteristics, and dynamic change trends. Subsequently, the geographic detector was employed to identify the key factors driving the spatio-temporal differentiation of smart farms and to discern the interactions between different factors. The analysis was conducted across seven dimensions: special fiscal support, industry dependence, human capital, urbanization, agricultural mechanization, internet infrastructure, and technological innovation. [Results and Discussions] Firstly, in terms of temporal characteristics, the number of smart farms in the Yangtze River Economic Belt steadily increased over the past decade. The year 2016 marked a significant turning point, after which the growth rate of smart farms had accelerated noticeably. The development of the upper, middle, and lower reaches exhibited both commonalities and disparities. Specifically, the lower sub-regions got a higher overall development level of smart farms, with a fluctuating upward growth rate; the middle sub-regions were at a moderate level, showing a fluctuating upward growth rate and relatively even provincial distribution; the upper sub-regions got a low development level, with a stable and slow growth rate, and an unbalanced provincial distribution. Secondly, in terms of spatial distribution, smart farms in the Yangtze River Economic Belt exhibited a dispersed agglomeration pattern. The results of global auto-correlation indicated that smart farms in the Yangtze River Economic Belt tended to be randomly distributed. The results of local auto-correlation showed that the predominant patterns of agglomeration were H-L and L-H types, with the distribution across provinces being somewhat complex; H-H type agglomeration areas were mainly concentrated in Sichuan, Hubei, and Anhui; L-L type agglomeration areas were primarily in Yunnan and Guizhou. The standard deviation ellipse results revealed that the mean center of smart farms in the Yangtze River Economic Belt had shifted from Anqing city in Anhui province in 2014 to Jingzhou city in Hubei province in 2023, with the spatial distribution showing an overall trend of shifting southwestward and a slow expansion toward the northeast and south. Finally, in terms of key driving factors, technological innovation was the primary critical factor driving the formation of the spatio-temporal distribution pattern of smart farms in the Yangtze River Economic Belt, with a factor explanatory degree of 0.311 1. Moreover, after interacting with other indicators, it continued to play a crucial role in the spatio-temporal distribution of smart farms, which aligned with the practical logic of smart farm development. Urbanization and agricultural mechanization levels were the second and third largest key factors, with factor explanatory degrees of 0.292 2 and 0.251 4, respectively. The key driving factors for the spatio-temporal differentiation of smart farms in the upper, middle, and lower sub-regions exhibited both commonalities and differences. Specifically, the top two key factors driver identification in the upper region were technological innovation (0.841 9) and special fiscal support (0.782 3). In the middle region, they were technological innovation (0.619 0) and human capital (0.600 1), while in the lower region, they were urbanization (0.727 6) and technological innovation (0.425 4). The identification of key driving factors and the detection of their interactive effects further confirmed that the spatio-temporal distribution characteristics of smart farms in the Yangtze River Economic Belt were the result of the comprehensive action of multiple factors. [Conclusions] The development of smart farms in the Yangtze River Economic Belt is showing a positive momentum, with both the total number of smart farms and the number of sub-regions experiencing stable growth. The development speed and level of smart farms in the sub-regions exhibit a differentiated characteristic of "lower reaches > middle reaches > upper reaches". At the same time, the overall distribution of smart farms in the Yangtze River Economic Belt is relatively balanced, with the degree of sub-regional distribution balance being "middle reaches (Hubei province, Hunan province, Jiangxi province are balanced) > lower reaches (dominated by Anhui) > upper reaches (Sichuan stands out)". The coverage of smart farm site selection continues to expand, forming a "northeast-southwest" horizontal diffusion pattern. In addition, the spatio-temporal characteristics of smart farms in the Yangtze River Economic Belt are the result of the comprehensive action of multiple factors, with the explanatory power of factors ranked from high to low as follows: Technological innovation > urbanization > agricultural mechanization > human capital > internet infrastructure > industry dependence > special fiscal support. Moreover, the influence of each factor is further strengthened after interaction. Based on these conclusions, suggestions are proposed to promote the high-quality development of smart farms in the Yangtze River Economic Belt. This study not only provides a theoretical basis and reference for the construction of smart farms in the Yangtze River Economic Belt and other regions, but also helps to grasp the current status and future trends of smart farm development.

[Objective] Acurately determining the suitable sowing date for winter wheat is of great significance for improving wheat yield and ensuring national food security. Traditional visual interpretation method is not only time-consuming and labor-intensive, but also covers a relatively small area. Remote sensing monitoring, belongs to post-event monitoring, exhibits a time lag. The aim of this research is to use the temperature threshold method and accumulated thermal time requirements for wheat leaves appearance method to analyze the suitable sowing date for winter wheat in county-level towns under the influence of long-term sequence of climate warming. [Methods] The research area were various townships in Qihe county, Shandong province. Based on European centre for medium-range weather forecasts (ECMWF) reanalysis data from 1997 to 2022, 16 meteorological data grid points in Qihe county were selected. Firstly, the bilinear interpolation method was used to interpolate the temperature data of grid points into the approximate center points of each township in Qihe county, and the daily average temperatures for each township were obtained. Then, temperature threshold method was used to determine the final dates of stable passage through 18, 16, 14 and 0 ℃. Key sowing date indicators such as suitable sowing temperature for different wheat varieties, growing degree days (GDD)≥0 ℃ from different sowing dates to before overwintering, and daily average temperature over the years were used for statistical analysis of the suitable sowing date for winter wheat. Secondly, the accumulated thermal time requirements for wheat leaves appearance method was used to calculate the appropriate date of GDD for strong seedlings before winter by moving forward from the stable date of dropping to 0 ℃. Accumulating the daily average temperatures above 0 ℃ to the date when the GDD above 0 ℃ was required for the formation of strong seedlings of wheat, a range of ±3 days around this calculated date was considered the theoretical suitable sowing date. Finally, combined with actual production practices, the appropriate sowing date of winter wheat in various townships of Qihe county was determined under the trend of climate warming. [Results and Discussions] The results showed that, from November 1997 to early December 2022, winter and annual average temperatures in Qihe county had all shown an upward trend, and there was indeed a clear trend of climate warming in various townships of Qihe county. Judging from the daily average temperature over the years, the temperature fluctuation range in November was the largest in a year, with a maximum standard deviation was 2.61 ℃. This suggested a higher likelihood of extreme weather conditions in November. Therefore, it was necessary to take corresponding measures to prevent and reduce disasters in advance to avoid affecting the growth and development of wheat. In extreme weather conditions, it was limited to determine the sowing date only by temperature or GDD. In cold winter years, it was too one-sided to consider only from the perspective of GDD. It was necessary to expand the range of GDD required for winter wheat before overwintering based on temperature changes to ensure the normal growth and development of winter wheat. The suitable sowing date for semi winter wheat obtained by temperature threshold method was from October 4th to October 16th, and the suitable sowing date for winter wheat was from September 27th to October 4th. Taking into account the GDD required for the formation of strong seedlings before winter, the suitable sowing date for winter wheat was from October 3rd to October 13th, and the suitable sowing date for semi winter wheat was from October 15th to October 24th, which was consisted with the suitable sowing date for winter wheat determined by the accumulated thermal time requirements for wheat leaves appearance method. Considering the winter wheat varieties planted in Qihe county, the optimal sowing date for winter wheat in Qihe county was from October 3rd to October 16th, and the optimal sowing date was from October 5th to October 13th. With the gradual warming of the climate, the suitable sowing date for wheat in various townships of Qihe county in 2022 was later than that in 2002. However, the sowing date for winter wheat was still influenced by factors such as soil moisture, topography, and seeding quality. The suitable sowing date for a specific year still needed to be adjusted to local conditions and flexibly sown based on the specific situation of that year. [Conclusions] The experimental results proved the feasibility of the temperature threshold method and accumulated thermal time requirements for wheat leaves appearance method in determining the suitable sowing date for winter wheat. The temperature trend can be used to identify cold or warm winters, and the sowing date can be adjusted in a timely manner to enhance wheat yield and reduce the impact of excessively high or low temperatures on winter wheat. The research results can not only provide decision-making reference for winter wheat yield assessment in Qihe county, but also provide an important theoretical basis for scientifically arrangement of agricultural production.

[Objective] Traditional object detection algorithms applied in the agricultural field, such as those used for crop growth monitoring and harvesting, often suffer from insufficient accuracy. This is particularly problematic for small crops like mushrooms, where recognition and detection are more challenging. The introduction of small object detection technology promises to address these issues, potentially enhancing the precision, efficiency, and economic benefits of agricultural production management. However, achieving high accuracy in small object detection has remained a significant challenge, especially when dealing with varying image sizes and target scales. Although the YOLO series models excel in speed and large object detection, they still have shortcomings in small object detection. To address the issue of maintaining high accuracy amid changes in image size and target scale, a novel detection model, Multi-Strategy Handling YOLOv8 (MSH-YOLOv8), was proposed. [Methods] The proposed MSH-YOLOv8 model builds upon YOLOv8 by incorporating several key enhancements aimed at improving sensitivity to small-scale targets and overall detection performance. Firstly, an additional detection head was added to increase the model's sensitivity to small objects. To address computational redundancy and improve feature extraction, the Swin Transformer detection structure was introduced into the input module of the head network, creating what was termed the "Swin Head (SH)". Moreover, the model integrated the C2f_Deformable convolutionv4 (C2f_DCNv4) structure, which included deformable convolutions, and the Swin Transformer encoder structure, termed "Swinstage", to reconstruct the YOLOv8 backbone network. This optimization enhanced feature propagation and extraction capabilities, increasing the network's ability to handle targets with significant scale variations. Additionally, the normalization-based attention module (NAM) was employed to improve performance without compromising detection speed or computational complexity. To further enhance training efficacy and convergence speed, the original loss function CIoU was replaced with wise-intersection over union (WIoU) Loss. Furthermore, experiments were conducted using mushrooms as the research subject on the open Fungi dataset. Approximately 200 images with resolution sizes around 600×800 were selected as the main research material, along with 50 images each with resolution sizes around 200×400 and 1 000×1 200 to ensure representativeness and generalization of image sizes. During the data augmentation phase, a generative adversarial network (GAN) was utilized for resolution reconstruction of low-resolution images, thereby preserving semantic quality as much as possible. In the post-processing phase, dynamic resolution training, multi-scale testing, soft non-maximum suppression (Soft-NMS), and weighted boxes fusion (WBF) were applied to enhance the model's small object detection capabilities under varying scales. [Results and Discussions] The improved MSH-YOLOv8 achieved an average precision at 50% (AP50) intersection over union of 98.49% and an AP@50-95 of 75.29%, with the small object detection metric APs reaching 39.73%. Compared to mainstream models like YOLOv8, these metrics showed improvements of 2.34%, 4.06% and 8.55%, respectively. When compared to the advanced TPH-YOLOv5 model, the improvements were 2.14%, 2.76% and 6.89%, respectively. The ensemble model, MSH-YOLOv8-ensemble, showed even more significant improvements, with AP50 and APs reaching 99.14% and 40.59%, respectively, an increase of 4.06% and 8.55% over YOLOv8. These results indicate the robustness and enhanced performance of the MSH-YOLOv8 model, particularly in detecting small objects under varying conditions. Further application of this methodology on the Alibaba Cloud Tianchi databases "Tomato Detection" and "Apple Detection" yielded MSH-YOLOv8-t and MSH-YOLOv8-a models (collectively referred to as MSH-YOLOv8). Visual comparison of detection results demonstrated that MSH-YOLOv8 significantly improved the recognition of dense and blurry small-scale tomatoes and apples. This indicated that the MSH-YOLOv8 method possesses strong cross-dataset generalization capability and effectively recognizes small-scale targets. In addition to quantitative improvements, qualitative assessments showed that the MSH-YOLOv8 model could handle complex scenarios involving occlusions, varying lighting conditions, and different growth stages of the crops. This demonstrates the practical applicability of the model in real-world agricultural settings, where such challenges are common. [Conclusions] The MSH-YOLOv8 improvement method proposed in this study effectively enhances the detection accuracy of small mushroom targets under varying image sizes and target scales. This approach leverages multiple strategies to optimize both the architecture and the training process, resulting in a robust model capable of high-precision small object detection. The methodology's application to other datasets, such as those for tomato and apple detection, further underscores its generalizability and potential for broader use in agricultural monitoring and management tasks.

[Objective] The pneumatic rapeseed seeder easily inhales the dust generated during live broadcast operations into the seeding pipe, and then releases it along with the rapeseed. Consequently, when monitoring the rapeseed flow, dust interference can affect the flow, detection sensitivity and accuracy. This research aims to develop a dust removal rapeseed seeding rate monitoring system suitable for air-assisted pneumatic rapeseed seeders to improve the transparency and intelligence of the sowing process. [Methods] The monitoring system comprised a dust removal rapeseed seed flow detection device and a sowing monitoring terminal, which could be adjusted for different seeders widths by altering the number of detection devices. The rapeseed stream sensing structure operated on the principle of photoelectric induction. A delicate light layer was generated using an LED light source and a narrow slit structure. The convex lens condenses the light and directed it onto the sensing area of the silicon photovoltaic cell. When the rapeseed seeds pass through the sensing light layer, the silicon photovoltaic cell produced a voltage change signal. The signal converts it into a pulse signal that can be recognized by the microcontroller. A dust removal mechanism was designed by analyzing dust sources in the seeding system during normal field operation of the air-assisted rapeseed seeding machine and understanding the impact mechanism of the dust detection device on the accuracy of rapeseed flow monitoring. This mechanism employed a transparent plate to protect the photoelectric induction device in a relatively enclosed space and used a stepper motor screw mechanism to generate friction between the transparent plate and the dust removal cloth for effective dust removal. The appropriate size of the dust shield was determined by comparing its movement stroke with other structural dimensions of the detection device. The relationship between the silicon photocells voltage and detection accuracy was established through experiments at seeding frequencies of 10‒40 Hz. To ensure that the real-time detection accuracy was not less than 90%, the dust removal control threshold was set at 82% of the initial voltage value. In order to prevent congestion and data loss during data transmission and improve the scalability and compatibility of the monitoring system, data transmission between the detection device and the monitoring terminal was implemented based on the CAN2.0A communication protocol. The structural framework and monitoring terminal functions of the rapeseed sowing monitoring system were outlined. Software functions of the detection device were designed to meet the dust removal, communication, and rapeseed flow detection needs. The program execution process of the detection device was explained. In order to provide data support for the dust flow rate that should be controlled at various seeding frequencies during the bench test, experiments were conducted in the field to obtain theoretical data. [Results and Discussions] The comparison bench test of the detection device indicates that with the average seeding frequency ranging from 12.4 to 36.3 Hz and the average dust flow rate ranging from 252 to 386 mg/s, the detection accuracy after two dust removal cycles without a dustproof and dust removal detection device was not higher than 80.2%. The dust detection device with dust removal got an accuracy rate of not less than 90.2%, and the average detection accuracy rate within a single dust removal cycle was not less than 93.6%. The seeding amount monitoring bench test showed that when the average seeding frequency was no higher than 37.6 Hz, the seeding rate monitoring accuracy was not less than 92.2%. Furthermore, the field sowing experiment results demonstrated that at a normal operating speed (2.8‒4.6 km/h) of the rapeseed direct seeder, with a field sowing frequency of 14.8‒31.1 Hz, the accuracy of sowing quantity monitoring was not less than 93.1%. [Conclusions] The rapeseed sowing quality monitoring system provides effective support for precise detection even when operating in dusty conditions with the pneumatic rapeseed direct seeder. In the future, by integrating positioning data, sowing information, and fertilization monitoring data through CAN bus technology, a comprehensive field sowing and fertilization status map can be created to further enhance the system's capabilities.

[Objective] The picking of famous and high-quality tea is a crucial link in the tea industry. Identifying and locating the tender buds of famous and high-quality tea for picking is an important component of the modern tea picking robot. Traditional neural network methods suffer from issues such as large model size, long training times, and difficulties in dealing with complex scenes. In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points. [Methods] The primary technical innovation resided in the amalgamation of a lightweight network architecture, MobilenetV2, with an attention mechanism known as efficient channel attention network (ECANet), alongside optimization modules including atrous spatial pyramid pooling (ASPP). Initially, MobilenetV2 was employed as the feature extractor, substituting traditional convolution operations with depth wise separable convolutions. This led to a notable reduction in the model's parameter count and expedited the model training process. Subsequently, the innovative fusion of ECANet and ASPP modules constituted the ECA_ASPP module, with the intention of bolstering the model's capacity for fusing multi-scale features, especially pertinent to the intricate recognition of tea shoots. This fusion strategy facilitated the model's capability to capture more nuanced features of delicate shoots, thereby augmenting segmentation accuracy. The specific implementation steps entailed the feeding of image inputs through the improved network, whereupon MobilenetV2 was utilized to extract both shallow and deep features. Deep features were then fused via the ECA_ASPP module for the purpose of multi-scale feature integration, reinforcing the model's resilience to intricate backgrounds and variations in tea shoot morphology. Conversely, shallow features proceeded directly to the decoding stage, undergoing channel reduction processing before being integrated with upsampled deep features. This divide-and-conquer strategy effectively harnessed the benefits of features at differing levels of abstraction and, furthermore, heightened the model's recognition performance through meticulous feature fusion. Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points. [Results and Discussions] The experimental outcomes indicated that the enhanced DeepLabV3+ model had achieved an average Intersection over Union (IoU) of 93.71% and an average pixel accuracy of 97.25% on the dataset of tea shoots. Compared to the original model based on Xception, there was a substantial decrease in the parameter count from 54.714 million to a mere 5.818 million, effectively accomplishing a significant lightweight redesign of the model. Further comparisons with other prevalent semantic segmentation networks revealed that the improved model exhibited remarkable advantages concerning pivotal metrics such as the number of parameters, training duration, and average IoU, highlighting its efficacy and precision in the domain of tea shoot recognition. This considerable decreased in parameter numbers not only facilitated a more resource-economical deployment but also led to abbreviated training periods, rendering the model highly suitable for real-time implementations amidst tea garden ecosystems. The elevated mean IoU and pixel accuracy attested to the model's capacity for precise demarcation and identification of tea shoots, even amidst intricate and varied datasets, demonstrating resilience and adaptability in pragmatic contexts. [Conclusions] This study effectively implements an efficient and accurate tea shoot recognition method through targeted model improvements and optimizations, furnishing crucial technical support for the practical application of intelligent tea picking robots. The introduction of lightweight DeepLabV3+ not only substantially enhances recognition speed and segmentation accuracy, but also mitigates hardware requirements, thereby promoting the practical application of intelligent picking technology in the tea industry.

[Objective] Sunlight-induced chlorophyll fluorescence (SIF) data obtained from satellites suffer from issues such as low spatial and temporal resolution, and discrete footprint because of the limitations imposed by satellite orbits. To address these problems, obtaining higher resolution SIF data, most reconstruction studies are based on low-resolution satellite SIF. Moreover, the spatial resolution of most SIF reconstruction products is still not enough to be directly used for the study of crop photosynthetic rate at the regional scale. Although some SIF products boast elevated resolutions, but these derive not from the original satellite SIF data reconstruct but instead evolve from secondary reconstructions based on preexisting SIF reconstruction products. Satellite OCO-2 (The Orbiting Carbon Obsevatory-2) equipped with a high-resolution spectrometer, OCO-2 SIF has higher spatial resolution (1.29×2.25 km) compared to other original SIF products, making it suitable in advancing the realm of high-resolution SIF data reconstruction, particularly within the context of regional-scale crop studies. [Methods] This research primarily exploration SIF reconstruct at the regional scale, mainly focused on the partial soybean planting regions nestled within the United States. The selection of MODIS raw data hinged on a meticulous consideration of environmental conditions, the distinctive physiological attributes of soybeans, and an exhaustive evaluation of factors intricately linked to OCO-2 SIF within these soybean planting regions. The primary tasks of this research encompassed reconstructing high resolution soybean SIF while concurrently executing a rigorous assessment of the reconstructed SIF's quality. During the dataset construction process, amalgamated SIF data from multiple soybean planting regions traversed by the OCO-2 satellite's footprint to retain as many of the available original SIF samples as possible. This approach provided the subsequent SIF reconstruction model with a rich source of SIF data. SIF data obtained beneath the satellite's trajectory were matched with various MODIS datasets, including enhanced vegetation index (EVI), fraction of photosynthetically active radiation (FPAR), and land surface temperature (LST), resulting in the creation of a multisource remote sensing dataset ultimately used for model training. Because of the multisource remote sensing dataset encompassed the most relevant explanatory variables within each SIF footprint coverage area concerning soybean physiological structure and environmental conditions. Through the activation functions in the BP neural network, it enhanced the understanding of the complex nonlinear relationships between the original SIF data and these MODIS products. Leveraging these inherent nonlinear relationships, compared and analyzed the effects of different combinations of explanatory variables on SIF reconstruction, mainly analyzing the three indicators of goodness of fit R², root mean square error RMSE, and mean absolute error MAE, and then selecting the best SIF reconstruction model, generate a regional scale, spatially continuous, and high temporal resolution (500 m, 8 d) soybean SIF reconstruction dataset (BPSIF). [Results and Discussions] The research findings confirmed the strong performance of the SIF reconstruction model in predicting soybean SIF. After simultaneously incorporating EVI, FPAR, and LST as explanatory variables to model, achieved a goodness of fit with an R² value of 0.84, this statistical metric validated the model's capability in predicting SIF data, it also reflected that the reconstructed 8 d time resolution of SIF data's reliability of applying to small-scale agricultural crop photosynthesis research with 500 m×500 m spatial scale. Based on this optimal model, generated the reconstructed SIF product (BPSIF). The Pearson correlation coefficient between the original OCO-2 SIF data and MODIS GPP stood were at a modest 0.53. In stark contrast, the correlation coefficient between BPSIF and MODIS Gross Primary Productivity (GPP) rosed significantly to 0.80. The increased correlation suggests that BPSIF could more accurately reflect the dynamic changes in GPP during the soybean growing season, making it more reliable compared to the original SIF data. Selected soybean planting areas in the United States with relatively single crop cultivation as the research area, based on high spatial resolution (1.29 km×2.25 km) OCO-2 SIF data, greatly reduced vegetation heterogeneity under a single SIF footprint. [Conclusions] The BPSIF proposed has significantly enhancing the regional and temporal continuity of OCO-2 SIF while preserving the time and spatial attributes contained in the original SIF dataset. Within the study area, BPSIF exhibits a significantly improved correlation with MODIS GPP compared to the original OCO-2 SIF. The proposed OCO-2 SIF data reconstruction method in this study holds the potential to provide a more reliable SIF dataset. This dataset has the potential to drive further understanding of soybean SIF at finer spatial and temporal scales, as well as find its relationship with soybean GPP.

[Objective] The number, location, and crown spread of nursery stock are important foundations data for their scientific management. Traditional approach of conducting nursery stock inventories through on-site individual plant surveys is labor-intensive and time-consuming. Low-cost and convenient unmanned aerial vehicles (UAVs) for on-site collection of nursery stock data are beginning to be utilized, and the statistical analysis of nursery stock information through technical means such as image processing achieved. During the data collection process, as the flight altitude of the UAV increases, the number of trees in a single image also increases. Although the anchor box can cover more information about the trees, the cost of annotation is enormous in the case of a large number of densely populated tree images. To tackle the challenges of tree adhesion and scale variance in images captured by UAVs over nursery stock, and to reduce the annotation costs, using point-labeled data as supervisory signals, an improved dense detection and counting model was proposed to accurately obtain the location, size, and quantity of the targets. [Method] To enhance the diversity of nursery stock samples, the spruce dataset, the Yosemite, and the KCL-London publicly available tree datasets were selected to construct a dense nursery stock dataset. A total of 1 520 nursery stock images were acquired and divided into training and testing sets at a ratio of 7:3. To enhance the model's adaptability to tree data of different scales and variations in lighting, data augmentation methods such as adjusting the contrast and resizing the images were applied to the images in the training set. After enhancement, the training set consists of 3 192 images, and the testing set contains 456 images. Considering the large number of trees contained in each image, to reduce the cost of annotation, the method of selecting the center point of the trees was used for labeling. The LSC-CNN model was selected as the base model. This model can detect the quantity, location, and size of trees through point-supervised training, thereby obtaining more information about the trees. The LSC-CNN model was made improved to address issues of missed detections and false positives that occurred during the testing process. Firstly, to address the issue of missed detections caused by severe adhesion of densely packed trees, the last convolutional layer of the feature extraction network was replaced with dilated convolution. This change enlarges the receptive field of the convolutional kernel on the input while preserving the detailed features of the trees. So the model is better able to capture a broader range of contextual information, thereby enhancing the model's understanding of the overall scene. Secondly, the convolutional block attention module (CBAM) attention mechanism was introduced at the beginning of each scale branch. This allowed the model to focus on the key features of trees at different scales and spatial locations, thereby improving the model's sensitivity to multi-scale information. Finally, the model was trained using label smooth cross-entropy loss function and grid winner-takes-all strategy, emphasizing regions with highest losses to boost tree feature recognition. [Results and Discussions] The mean counting accuracy (MCA), mean absolute error (MAE), and root mean square error (RMSE) were adopted as evaluation metrics. Ablation studies and comparative experiments were designed to demonstrate the performance of the improved LSC-CNN model. The ablation experiment proved that the improved LSC-CNN model could effectively resolve the issues of missed detections and false positives in the LSC-CNN model, which were caused by the density and large-scale variations present in the nursery stock dataset. IntegrateNet, PSGCNet, CANet, CSRNet, CLTR and LSC-CNN models were chosen as comparative models. The improved LSC-CNN model achieved MCA, MAE, and RMSE of 91.23%, 14.24, and 22.22, respectively, got an increase in MCA by 6.67%, 2.33%, 6.81%, 5.31%, 2.09% and 2.34%, respectively; a reduction in MAE by 21.19, 11.54, 18.92, 13.28, 11.30 and 10.26, respectively; and a decrease in RMSE by 28.22, 28.63, 26.63, 14.18, 24.38 and 12.15, respectively, compared to the IntegrateNet, PSGCNet, CANet, CSRNet, CLTR and LSC-CNN models. These results indicate that the improved LSC-CNN model achieves high counting accuracy and exhibits strong generalization ability. [Conclusions] The improved LSC-CNN model integrated the advantages of point supervision learning from density estimation methods and the generation of target bounding boxes from detection methods.These improvements demonstrate the enhanced performance of the improved LSC-CNN model in terms of accuracy, precision, and reliability in detecting and counting trees. This study could hold practical reference value for the statistical work of other types of nursery stock.

[Objective] Crop leaf area is an important indicator reflecting light absorption efficiency and growth conditions. This paper established a diverse Chinese yam image dataset and proposesd a deep learning-based method for Chinese yam leaf image segmentation. This method can be used for real-time measurement of Chinese yam leaf area, addressing the inefficiency of traditional measurement techniques. This will provide more reliable data support for genetic breeding, growth and development research of Chinese yam, and promote the development and progress of the Chinese yam industry. [Methods] A lightweight segmentation network based on improved ENet was proposed. Firstly, based on ENet, the third stage was pruned to reduce redundant calculations in the model. This improved the computational efficiency and running speed, and provided a good basis for real-time applications. Secondly, PConv was used instead of the conventional convolution in the downsampling bottleneck structure and conventional bottleneck structure, the improved bottleneck structure was named P-Bottleneck. PConv applied conventional convolution to only a portion of the input channels and left the rest of the channels unchanged, which reduced memory accesses and redundant computations for more efficient spatial feature extraction. PConv was used to reduce the amount of model computation while increase the number of floating-point operations per second on the hardware device, resulting in lower latency. Additionally, the transposed convolution in the upsampling module was improved to bilinear interpolation to enhance model accuracy and reduce the number of parameters. Bilinear interpolation could process images smoother, making the processed images more realistic and clear. Finally, coordinate attention (CA) module was added to the encoder to introduce the attention mechanism, and the model was named CBPA-ENet. The CA mechanism not only focused on the channel information, but also keenly captured the orientation and position-sensitive information. The position information was embedded into the channel attention to globally encode the spatial information, capturing the channel information along one spatial direction while retaining the position information along the other spatial direction. The network could effectively enhance the attention to important regions in the image, and thus improve the quality and interpretability of segmentation results. [Results and Discussions] Trimming the third part resulted in a 28% decrease in FLOPs, a 41% decrease in parameters, and a 9 f/s increase in FPS. Improving the upsampling method to bilinear interpolation not only reduces the floating-point operation and parameters, but also slightly improves the segmentation accuracy of the model, increasing FPS by 4 f/s. Using P-Bottleneck instead of downsampling bottleneck structure and conventional bottleneck structure can reduce mIoU by only 0.04%, reduce FLOPs by 22%, reduce parameters by 16%, and increase FPS by 8 f/s. Adding CA mechanism to the encoder could only increase a small amount of FLOPs and parameters, improving the accuracy of the segmentation network. To verify the effectiveness of the improved segmentation algorithm, classic semantic segmentation networks of UNet, DeepLabV3+, PSPNet, and real-time semantic segmentation network LinkNet, DABNet were selected to train and validate. These six algorithms got quite high segmentation accuracy, among which UNet had the best mIoU and the mPA, but the model size was too large. The improved algorithm only accounts for 1% of the FLOPs and 0.41% of the parameters of UNet, and the mIoU and mPA were basically the same. Other classic semantic segmentation algorithms, such as DeepLabV3+, had similar accuracy to improved algorithms, but their large model size and slow inference speed were not conducive to embedded development. Although the real-time semantic segmentation algorithm LinkNet had a slightly higher mIoU, its FLOPs and parameters count were still far greater than the improved algorithm. Although the PSPNet model was relatively small, it was also much higher than the improved algorithm, and the mIoU and mPA were lower than the algorithm. The experimental results showed that the improved model achieved a mIoU of 98.61%. Compared with the original model, the number of parameters and FLOPs significantly decreased. Among them, the number of model parameters decreased by 51%, the FLOPs decreased by 49%, and the network operation speed increased by 38%. [Conclusions] The improved algorithm can accurately and quickly segment Chinese yam leaves, providing not only a more accurate means for determining Chinese yam phenotype data, but also a new method and approach for embedded research of Chinese yam. Using the model, the morphological feature data of Chinese yam leaves can be obtained more efficiently, providing a reliable foundation for further research and analysis.

[Objective] Spraying calcium can effectively prevent the occurrence of dry burning heart disease in Chinese cabbage. Accurately targeting spraying calcium can more effectively improve the utilization rate of calcium. Since the sprayer needs to move rapidly in the field, this can lead to over-application or under-application of the pesticide. This study aims to develop a targeted spray control system based on deep learning technology, explore the relationship between the advance speed, spray volume, and coverage of the sprayer, thereby addressing the uneven application issues caused by different nebulizer speeds by studying the real scenario of calcium administration to Chinese cabbage hearts. [Methods] The targeted spraying control system incorporates advanced sensors and computing equipment that were capable of obtaining real-time data regarding the location of crops and the surrounding environmental conditions. This data allowed for dynamic adjustments to be made to the spraying system, ensuring that pesticides were delivered with high precision. To further enhance the system's real-time performance and accuracy, the YOLOv8 object detection model was improved. A Ghost-Backbone lightweight network structure was introduced, integrating remote sensing technologies along with the sprayer's forward speed and the frequency of spray responses. This innovative combination resulted in the creation of a YOLOv8-Ghost-Backbone lightweight model specifically tailored for agricultural applications. The model operated on the Jetson Xavier NX controller, which was a high-performance, low-power computing platform designed for edge computing. The system was allowed to process complex tasks in real time directly in the field. The targeted spraying system was composed of two essential components: A pressure regulation unit and a targeted control unit. The pressure regulation unit was responsible for adjusting the pressure within the spraying system to ensure that the output remains stable under various operational conditions. Meanwhile, the targeted control unit played a crucial role in precisely controlling the direction, volume, and coverage of the spray to ensure that the pesticide was applied effectively to the intended areas of the plants. To rigorously evaluate the performance of the system, a series of intermittent spray tests were conducted. During these tests, the forward speed of the sprayer was gradually increased, allowing to assess how well the system responded to changes in speed. Throughout the testing phase, the response frequency of the electromagnetic valve was measured to calculate the corresponding spray volume for each nozzle. [Results and Conclusions] The experimental results indicated that the overall performance of the targeted spraying system was outstanding, particularly under conditions of high-speed operation. By meticulously recording the response times of the three primary components of the system, the valuable data were gathered. The average time required for image processing was determined to be 29.50 ms, while the transmission of decision signals took an average of 6.40 ms. The actual spraying process itself required 88.83 ms to complete. A thorough analysis of these times revealed that the total response time of the spraying system lagged by approximately 124.73 ms when compared to the electrical signal inputs. Despite the inherent delays, the system was able to maintain a high level of spraying accuracy by compensating for the response lag of the electromagnetic valve. Specifically, when tested at a speed of 7.2 km/h, the difference between the actual spray volume delivered and the required spray volume, after accounting for compensation, was found to be a mere 0.01 L/min. This minimal difference indicates that the system met the standard operational requirements for effective pesticide application, thereby demonstrating its precision and reliability in practical settings. [Conclusions] In conclusion, this study developed and validated a deep learning-based targeted spraying control system that exhibited excellent performance regarding both spraying accuracy and response speed. The system serves as a significant technical reference for future endeavors in agricultural automation. Moreover, the research provides insights into how to maintain consistent spraying effectiveness and optimize pesticide utilization efficiency by dynamically adjusting the spraying system as the operating speed varies. The findings of this research will offer valuable experiences and guidance for the implementation of agricultural robots in the precise application of pesticides, with a particular emphasis on parameter selection and system optimization.

[Objective] Plug tray seedling cultivation is a contemporary method known for its high germination rates, uniform seedling growth, shortened transplant recovery period, diminished pest and disease incidence, and enhanced labor efficiency. Despite these advantages, challenges such as missing or underdeveloped seedlings can arise due to seedling quality and environmental factors. To ensure uniformity and consistency of the seedlings, sorting is frequently necessary, and the adoption of automated seedling sorting technology can significantly reduce labor costs. Nevertheless, the overgrowth of seedlings within the plugs can effect the accuracy of detection algorithms. A method for grading and locating strawberry seedlings based on a lightweight YOLOv8s model was presented in this research to effectively mitigate the interference caused by overgrown seedlings. [Methods] The YOLOv8s model was selected as the baseline for detecting different categories of seedlings in the strawberry plug tray cultivation process, namely weak seedlings, normal seedlings, and plug holes. To improve the detection efficiency and reduce the model's computational cost, the layer-adaptive magnitude-based pruning(LAMP) score-based channel pruning algorithm was applied to compress the base YOLOv8s model. The pruning procedure involved using the dependency graph to derive the group matrices, followed by normalizing the group importance scores using the LAMP Score, and ultimately pruning the channels according to these processed scores. This pruning strategy effectively reduced the number of model parameters and the overall size of the model, thereby significantly enhancing its inference speed while maintaining the capability to accurately detect both seedlings and plug holes. Furthermore, a two-stage seedling-hole matching algorithm was introduced based on the pruned YOLOv8s model. In the first stage, seedling and plug hole bounding boxes were matched according to their the degree of overlap (Dp), resulting in an initial set of high-quality matches. This step helped minimize the number of potential matching holes for seedlings exhibiting overgrowth. Subsequently, before the second stage of matching, the remaining unmatched seedlings were ranked according to their potential matching hole scores (S), with higher scores indicating fewer potential matching holes. The seedlings were then prioritized during the second round of matching based on these scores, thus ensuring an accurate pairing of each seedling with its corresponding plug hole, even in cases where adjacent seedling leaves encroached into neighboring plug holes. [Results and Discussions] The pruning process inevitably resulted in the loss of some parameters that were originally beneficial for feature representation and model generalization. This led to a noticeable decline in model performance. However, through meticulous fine-tuning, the model's feature expression capabilities were restored, compensating for the information loss caused by pruning. Experimental results demonstrated that the fine-tuned model not only maintained high detection accuracy but also achieved significant reductions in FLOPs (86.3%) and parameter count (95.4%). The final model size was only 1.2 MB. Compared to the original YOLOv8s model, the pruned version showed improvements in several key performance metrics: precision increased by 0.4%, recall by 1.2%, mAP by 1%, and the F₁-Score by 0.1%. The impact of the pruning rate on model performance was found to be non-linear. As the pruning rate increased, model performance dropped significantly after certain crucial channels were removed. However, further pruning led to a reallocation of the remaining channels' weights, which in some cases allowed the model to recover or even exceed its previous performance levels. Consequently, it was necessary to experiment extensively to identify the optimal pruning rate that balanced model accuracy and speed. The experiments indicated that when the pruning rate reached 85.7%, the mAP peaked at 96.4%. Beyond this point, performance began to decline, suggesting that this was the optimal pruning rate for achieving a balance between model efficiency and performance, resulting in a model size of 1.2 MB. To further validate the improved model's effectiveness, comparisons were conducted with different lightweight backbone networks, including MobileNetv3, ShuffleNetv2, EfficientViT, and FasterNet, while retaining the Neck and Head modules of the original YOLOv8s model. Results indicated that the modified model outperformed these alternatives, with mAP improvements of 1.3%, 1.8%, 1.5%, and 1.1%, respectively, and F₁-Score increases of 1.5%, 1.8%, 1.1%, and 1%. Moreover, the pruned model showed substantial advantages in terms of floating-point operations, model size, and parameter count compared to these other lightweight networks. To verify the effectiveness of the proposed two-stage seedling-hole matching algorithm, tests were conducted using a variety of complex images from the test set. Results indicated that the proposed method achieved precise grading and localization of strawberry seedlings even under challenging overgrowth conditions. Specifically, the correct matching rate for normal seedlings reached 96.6%, for missing seedlings 84.5%, and for weak seedlings 82.9%, with an average matching accuracy of 88%, meeting the practical requirements of the strawberry plug tray cultivation process. [Conclusions] The pruned YOLOv8s model successfully maintained high detection accuracy while reducing computational costs and improving inference speed. The proposed two-stage seedling-hole matching algorithm effectively minimized the interference caused by overgrown seedlings, accurately locating and classifying seedlings of various growth stages within the plug tray. The research provides a robust and reliable technical solution for automated strawberry seedling sorting in practical plug tray cultivation scenarios, offering valuable insights and technical support for optimizing the efficiency and precision of automated seedling grading systems.

[Objective] In the era of digital agriculture, real-time monitoring and predictive modeling of crop growth are paramount, especially in autonomous farming systems. Traditional crop growth models, often constrained by their reliance on static, rule-based methods, fail to capture the dynamic and multifactorial nature of vegetable crop growth. This research tried to address these challenges by leveraging the advanced reasoning capabilities of pre-trained large language models (LLMs) to simulate and predict vegetable crop growth with accuracy and reliability. Modeling the growth of vegetable crops within these platforms has historically been hindered by the complex interactions among biotic and abiotic factors. [Methods] The methodology was structured in several distinct phases. Initially, a comprehensive dataset was curated to include extensive information on vegetable crop growth cycles, environmental conditions, and management practices. This dataset incorporates continuous data streams such as soil moisture, nutrient levels, climate variables, pest occurrence, and historical growth records. By combining these data sources, the study ensured that the model was well-equipped to understand and infer the complex interdependencies inherent in crop growth processes. Then, advanced techniques was emploied for pre-training and fine-tuning LLMs to adapt them to the domain-specific requirements of vegetable crop modeling. A staged intelligent agent ensemble was designed to work within the digital twin platform, consisting of a central managerial agent and multiple stage-specific agents. The managerial agent was responsible for identifying transitions between distinct growth stages of the crops, while the stage-specific agents were tailored to handle the unique characteristics of each growth phase. This modular architecture enhanced the model's adaptability and precision, ensuring that each phase of growth received specialized attention and analysis. [Results and Discussions] The experimental validation of this method was conducted in a controlled agricultural setting at the Xiaotangshan Modern Agricultural Demonstration Park in Beijing. Cabbage (Zhonggan 21) was selected as the test crop due to its significance in agricultural production and the availability of comprehensive historical growth data. Over five years, the dataset collected included 4 300 detailed records, documenting parameters such as plant height, leaf count, soil conditions, irrigation schedules, fertilization practices, and pest management interventions. This dataset was used to train the LLM-based system and evaluate its performance using ten-fold cross-validation. The results of the experiments demonstrating the efficacy of the proposed system in addressing the complexities of vegetable crop growth modeling. The LLM-based model achieved 98% accuracy in predicting crop growth degrees and a 99.7% accuracy in identifying growth stages. These metrics significantly outperform traditional machine learning approaches, including long short-term memory (LSTM), XGBoost, and LightGBM models. The superior performance of the LLM-based system highlights its ability to reason over heterogeneous data inputs and make precise predictions, setting a new benchmark for crop modeling technologies. Beyond accuracy, the LLM-powered system also excels in its ability to simulate growth trajectories over extended periods, enabling farmers and agricultural managers to anticipate potential challenges and make proactive decisions. For example, by integrating real-time sensor data with historical patterns, the system can predict how changes in irrigation or fertilization practices will impact crop health and yield. This predictive capability is invaluable for optimizing resource allocation and mitigating risks associated with climate variability and pest outbreaks. [Conclusions] The study emphasizes the importance of high-quality data in achieving reliable and generalizable models. The comprehensive dataset used in this research not only captures the nuances of cabbage growth but also provides a blueprint for extending the model to other crops. In conclusion, this research demonstrates the transformative potential of combining large language models with digital twin technology for vegetable crop growth modeling. By addressing the limitations of traditional modeling approaches and harnessing the advanced reasoning capabilities of LLMs, the proposed system sets a new standard for precision agriculture. Several avenues also are proposed for future work, including expanding the dataset, refining the model architecture, and developing multi-crop and multi-region capabilities.

[Objective] Apple Alternaria leaf spot can easily lead to premature defoliation of apple tree leaves, thereby affecting the quality and yield of apples. Consequently, accurately detecting of the disease has become a critical issue in the precise prevention and control of apple tree diseases. Due to factors such as backlighting, traditional image segmentation-based methods for detecting disease spots struggle to accurately identify the boundaries of diseased areas against complex backgrounds. There is an urgent need to develop new methods for detecting apple Alternaria leaf spot, which can assist in the precise prevention and control of apple tree diseases. [Methods] A novel detection method named Deep Semi-Non-negative Matrix Factorization-based Mahalanobis Distance Anomaly Detection (DSNMFMAD) was proposed, which combines Deep Semi-Non-negative Matrix Factorization (DSNMF) with Mahalanobis distance for robust anomaly detection in complex image backgrounds. The proposed method began by utilizing DSNMF to extract low-rank background components and sparse anomaly features from the apple Alternaria leaf spot images. This enabled effective separation of the background and anomalies, mitigating interference from complex background noise while preserving the non-negativity constraints inherent in the data. Subsequently, Mahalanobis distance was employed, based on the Singular Value Decomposition (SVD) feature subspace, to construct a lesion detector. The detector identified lesions by calculating the anomaly degree of each pixel in the anomalous regions. The apple tree leaf disease dataset used was provided by PaddlePaddle AI-Studio. Each image in the dataset has a resolution of 512×512 pixels, in RGB color format, and was in JPEG format. The dataset was captured in both laboratory and natural environments. Under laboratory conditions, 190 images of apple leaves with spot-induced leaf drop were used, while 237 images were collected under natural conditions. Furthermore, the dataset was augmented with geometric transformations and random changes in brightness, contrast, and hue, resulting in 1 145 images under laboratory conditions and 1 419 images under natural conditions. These images reflect various real-world scenarios, capturing apple leaves at different stages of maturity, in diverse lighting conditions, angles, and noise environments. This diversed dataset ensured that the proposed method could be tested under a wide range of practical conditions, providing a comprehensive evaluation of its effectiveness in detecting apple Alternaria leaf spot. [Results and Discussions] DSNMFMAD demonstrated outstanding performance under both laboratory and natural conditions. A comparative analysis was conducted with several other detection methods, including GRX (Reed-Xiaoli detector), LRX (Local Reed-Xiaoli detector), CRD (Collaborative-Representation-Based Detector), LSMAD (LRaSMD-Based Mahalanobis Distance Detector), and the deep learning model Unet. The results demonstrated that DSNMFMAD exhibited superior performance in the laboratory environment. The results demonstrated that DSNMFMAD attained a recognition accuracy of 99.8% and a detection speed of 0.087 2 s/image. The accuracy of DSNMFMAD was found to exceed that of GRX, LRX, CRD, LSMAD, and Unet by 0.2%, 37.9%, 10.3%, 0.4%, and 24.5%, respectively. Additionally, the DSNMFMAD exhibited a substantially superior detection speed in comparison to LRX, CRD, LSMAD, and Unet, with an improvement of 8.864, 107.185, 0.309, and 1.565 s, respectively. In a natural environment, where a dataset of 1 419 images of apple Alternaria leaf spot was analysed, DSNMFMAD demonstrated an 87.8% recognition accuracy, with an average detection speed of 0.091 0 s per image. In this case, its accuracy outperformed that of GRX, LRX, CRD, LSMAD, and Unet by 2.5%, 32.7%, 5%, 14.8%, and 3.5%, respectively. Furthermore, the detection speed was faster than that of LRX, CRD, LSMAD, and Unet by 2.898, 132.017, 0.224, and 1.825 s, respectively. [Conclusions] The DSNMFMAD proposed in this study was capable of effectively extracting anomalous parts of an image through DSNMF and accurately detecting the location of apple Alternaria leaf spot using a constructed lesion detector. This method achieved higher detection accuracy compared to the benchmark methods, even under complex background conditions, demonstrating excellent performance in lesion detection. This advancement could provide a valuable technical reference for the detection and prevention of apple Alternaria leaf spot.