[Objective] Crop diseases and pests are significant factors restricting global agricultural production. Traditional intelligent recognition technologies predominantly rely on single-modal image data processed by convolutional neural networks (CNNs) or Transformers. However, in complex natural environments, these methods often suffer from insufficient information utilization and limited robustness due to the lack of semantic guidance. Although emerging multimodal approaches like CLIP have introduced textual information, they typically rely on shallow feature alignment in the embedding space without achieving deep semantic interaction or effective feature fusion. Furthermore, the asymmetry between the quantity of image samples and text labels during training poses a challenge for effective cross-modal learning. In this study, a self-supervised adaptive multimodal feature fusion recognition (SAFusion-CLIP) method is proposed, aiming to significantly enhance classification accuracy and model generalization in fine-grained diseases and pests recognition tasks. [Methods] A comprehensive recognition framework was constructed, integrating four key components to achieve deep fusion of visual and textual features. First, prompt engineering was conducted by utilizing large language models (LLMs) combined with authoritative agricultural guides to transform simple category labels into fine-grained pathological semantic descriptions. These descriptions encapsulated morphological details, color gradients, and texture features, with quality verified by BERTScore and ROUGE-L metrics. Second, a cross-modal balanced alignment module was designed to resolve the problem of sample asymmetry between image batches and fixed text labels. This module employed a dot-product attention mechanism to calculate the correlation between image and text projections, applying Softmax normalization to dynamically align image features with their corresponding textual representations. Third, an adaptive fusion mechanism was employed to achieve deep semantic interaction. A gating unit based on the Sigmoid function was designed to calculate a gate value, which dynamically allocated weights to image and text features, allowing the model to adaptively integrate complementary information from both modalities. Finally, a self-supervised feature reconstruction task was introduced to enhance the robustness of feature representation. A simple decoder was utilized to reconstruct the original image and text embeddings from the fused features, and the model was optimized using a composite objective function combining image-text contrastive loss, mean squared error reconstruction loss, and weighted cross-entropy classification loss. [Results and Discussions] Extensive experiments were conducted on the standard PlantVillage dataset, which includes 39 categories covering 14 crop species. The proposed SAFusion-CLIP model achieved a classification accuracy of 99.67%, with precision, recall, and F1-Score all exceeding 99.00%. Comparative analysis demonstrated that the proposed method significantly outperformed mainstream single-modal and baseline multimodal models, ResNet50 (96.51%), Swin-Transformer (97.48%), and baseline CLIP (98.23%), respectively. Visualization analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) indicated that, unlike single-modal models which were susceptible to background noise or non-specific physical damage, the SAFusion-CLIP model focused more precisely on core lesion areas, effectively suppressing background interference. Furthermore, ablation studies confirmed the effectiveness of the proposed modules, showing that the combination of the self-supervised architecture and the adaptive fusion mechanism resulted in a 2.46 percentage points accuracy improvement over the baseline, validating the necessity of deep feature interaction and reconstruction tasks. [Conclusions] By fusing textual semantics with visual features, the SAFusion-CLIP method effectively overcame the limitations of single-modal recognition. The adaptive fusion mechanism ensured deep interaction between modalities, while the self-supervised reconstruction task significantly enhanced the robustness of feature representation. The experimental results verified that this data-driven approach significantly improves accuracy and generalization capabilities in fine-grained crop disease classification tasks, providing a new and effective solution for precision agricultural prevention and control.
[Significance] With the advancement of technology and diversified consumer demands, traditional agriculture is gradually transforming towards information and intelligence. Conducting research on intelligent management and control technologies for facility vegetable production is of great significance for improving vegetable yield and quality, ensuring stable market supply, and promoting high-quality development of the vegetable industry. The purpose of this article is to systematically collate the research status, key technologies and application constraints in the field of intelligent management and control of facility vegetables. By analyzing the development trends of environmental regrlation, growth monitoring and precise management, it provides scientific basis, theoretical support and decision-making references for the intelligent upgrading, technological innovation and policy formulation of Chinese facility vegetable industry, so as to boost the high-quality and sustainable development of facility agriculture. [Progress] This paper systematically analyzes the innovative applications of information technologies such as Internet of Things, block chain, and artificial intelligence in critical domains of facility vegetable production information, including precise regulation of the production environment, intelligent cultivation management and smart storage information management. In terms of precise regulation of the production environment, a temperature and humidity model for the optimal growth environment of tomatoes has been established, primarily utilizing Internet of Things technology. This enables precise monitoring and intelligent control of environmental parameters such as temperature, humidity, light, and carbon dioxide concentration within the facility, creating an optimal environment for vegetable growth. In the field of intelligent cultivation management, the integration of intelligent integrated water and fertilizer equipment, agricultural robotic operation systems, and pest and disease control has optimized the whole-process information-based management, effectively improving cultivation efficiency and vegetable quality. The integrated water and fertilizer systems apply Internet on Things technology to coordinate irrigation and fertilization through digital methods. Agricultural robotic operation systems are based on artificial intelligence, encompassing technologies such as machine learning, deep learning, neural networks and image processing. The pest and disease control section highlights the information-based applications in physical control, biological control and chemical control. In terms of smart storage information management, the application of origin storage preservation technology, intelligent classification and sorting systems, as well as traceability information platforms has significantly enhanced the circulation quality and safety assurance level of vegetables. Specifically, the origin storage preservation technology focuses on the development status of pre-cooling preservation, controlled atmosphere preservation, biological preservation and coating preservation. Intelligent grading and sorting technologies are categorized into non-destructive testing for both the external and internal quality of vegetables. The traceability information platform, leveraging blockchain and large model technologies, enables more intelligent management of facility vegetable production. [Conclusions and Prospects] This paper explores the problems encountered in the development of intelligent management and control technology for protected vegetables, including insufficient accuracy and stability of sensors, lagging regulatory decision-making, lack of equipment coordination mechanisms, poor integration of pest and disease control, fragmentation of information in the whole process of storage, difficulty in quality traceability, and lagging risk warning. Corresponding countermeasures and suggestions are proposed as follows: optimization of hardware, multi-technology integration to support precise perception and intelligent regulation, enhancement of equipment coordination and optimization, integration of pest and disease control, and construction of a virtual-real interactive storage management system through the integration of digital twins and metaverse. Finally, the paper prospects the future development direction of facility vegetable in precise control of production environment, cultivation management information, and storage information control.
[Significance] In alignment with the national germplasm security strategy, current research efforts are accelerating the adoption of precision breeding in sheep. Within the whole-genome selection, accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value. Traditional manual measurements are inefficient, prone to human error, and may cause stress to sheep, limiting their suitability for precision sheep management. By summarizing the applications of sheep body size measurement technologies and analyzing their development directions, this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement. [Progress] This review synthesizes progress across three principal methodological paradigms: two-dimensional (2D) image-based techniques, three-dimensional (3D) point cloud-based approaches, and integrated 2D-3D fusion systems. 2D methods, employing either handcrafted geometric features or deep learning-based keypoint detector algorithms, are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics. 3D point-cloud approaches enable precise reconstruction of full animal morphology, supporting comprehensive body-size acquisition with higher accuracy, yet face challenges including high hardware costs, complex data workflows, and sensitivity to posture variability. Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds. Having been effectively validated in other livestock specise, e.g., cattle and pigs, these fusion systems have demonstrated excellent performance, providing important technical references and practical insights for sheep body size measurement. [Conclusions and Prospects] Firstly, future research should focus on constructing large-scale, high-quality datasets for sheep body size measurement that encompass diverse breeds, growth stages, and environmental conditions, thereby enhancing model robustness and generalization. Secondly, the development of lightweight artificial intelligence models is essential. Techniques such as model compression, quantization, and algorithmic optimization can substantially reduce computational complexity and storage requirements, facilitating deployment in resource-constrained environments. Thirdly, the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition, filtering, registration, and segmentation, while promoting the integration of low-cost, high-resilience vision systems into practical farming scenarios. Fourthly, specific emphasis should be placed on improving the accuracy of curved-dimensional measurements, such as chest circumference, abdominal circumference, and shank circumference, through advances in pose standardization, refined 3D segmentation strategies, and multi-modal data fusion. Finally, the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation, accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent, data-driven pasture management practices.
[Objective] Timely detection and early warning of livestock health issues are critical for green and efficient management within large-scale cattle farms. Traditional manual inspections are time-consuming, labor-intensive, and prone to missed or erroneous detections. Robotic inspections offer significant advantages including all-weather operation, high precision, high efficiency, and low cost. However, existing path planning approaches predominantly focus on dynamic obstacle avoidance and fixed target point inspection path, often failing to address two key challenges in dynamic large-scale farm environments: global traversal of individual large livestock (e.g., beef cattle, dairy cows) and accessibility of local areas compromised by dynamic obstacles. This study aims to overcome the limitations of existing robotic inspection systems in large-scale cattle farms, specifically addressing the lack of comprehensive inspection capability for dynamic individuals, excessive path redundancy, and insufficient proactive obstacle avoidance capability. [Methods] A global-local optimization algorithm was proposed for large-scale cattle farm intelligent inspection path planning, which integrated the traveling salesman problem (TSP), A* and dynamic window approach (DWA), and solved the problems of global multi-objective individual traversal, path redundancy and local passability with proactive obstacle avoidance in dynamic cattle farm scenarios. For global traversal optimization, a global path planning algorithm was introduced which combined improved TSP and optimized A*. Specifically, the inspection status list tracking breeding sheds and individual cattle was maintained to enhance the TSP's Nearest Neighbor Algorithm, dynamically updating targets to avoid re-visits. A dynamic priority mechanism optimized multi-objective inspection, determining the optimal visitation sequence across barns and dynamic paths within barns. The data structure of the A* algorithm was optimized, a diagonal distance heuristic function was introduced to replace Manhattan distance, which more accurately reflected the movement cost in eight directions. The path obtained by the A* algorithm through greedy strategy was simplified, and Bresenham's line algorithm was used to check whether there were obstacles in the straight line field of view. If there were no obstacles, redundant inflection points were removed to construct an efficient moving path between sheds. For local passability optimization, an enhanced DWA-based local path was proposed for planning algorithm. The dynamic safety threshold of obstacle size was introduced to improve the DWA. When the inspection robot judged that the size of the obstacle in the local accessible area was too large and the robot was difficult to pass, it would actively avoid or detour in advance to ensure the safe avoidance of large obstacles in narrow passages. The improved DWA also increased the task progress potential field, drived the robot to move to the breeding shed to be visited with the attractive force field model, balanced the local obstacle avoidance and global inspection efficiency, and realized the real-time judgment of local area passability caused by dynamic obstacles and proactive obstacle avoidance in advance. [Results and Discussions] The optimized A* algorithm's data structures significantly improved search efficiency. The diagonal distance heuristic and greedy strategy substantially enhanced path smoothness. Compared to the traditional A*, the improved A* achieved average reductions of 90.06% in planning time, 85.13% in path turns, and 1.83% in path length. The global inspection algorithm combining improved TSP and optimized A* achieved 100% average coverage of individual cattle. Inspection path length and time were reduced by 17.99% and 20.85%, respectively, compared to the classic ant colony optimization (ACO) algorithm, demonstrating superior efficiency in dynamic multi-objective inspection scenarios. The improved DWA successfully enabled proactive judgment of local path passability based on obstacle size. By adjusting the robot's linear velocity, angular velocity, and attitude angle in real time, the algorithm achieved robust proactive obstacle avoidance. The inspection robot would reduce the linear velocity in advance when encountering obstacles, and realize proactive obstacle avoidance by adjusting the attitude angle. Simulation experiments confirmed that robots equipped with the improved DWA effectively navigated around unknown static and dynamic obstacles while maintaining global path-tracking capability. [Conclusions] The global inspection algorithm combining improved TSP and optimized A*, utilizing dynamic inspection status lists and path optimization techniques, achieved global inspection coverage of individual cattle and could significantly improve inspection quality and efficiency. The local inspection algorithm based on improved DWA, incorporating obstacle size dynamic safety threshold and task progress, achieved real-time judgment of local passability and proactive obstacle avoidance, ensuring safe robot navigation in complex environments. The global-local co-optimization framework demonstrated adaptability to the dynamic farm environment, enabling the timely completion of individual traversal tasks, and providing a robust solution for intelligent inspection in large-scale cattle operations. Future work involves integrating the proposed path planning algorithm with simultaneous localization and mapping (SLAM), cattle identification, distance detection systems on inspection robot platforms, and conducting extensive field tests within operational cattle farms. Exploring multi-robot collaborative inspection frameworks and incorporating the Vision-and-Language Navigation model to enhance environmental perception and anomaly-handling capabilities are promising directions for adapting to the complexities of even larger-scale farming scenarios.
[Objective] With the accelerated development of large-scale and intelligent aquaculture, accurate estimation of the body weight of individual Chinese mitten crabs is critical for tasks such as precise feeding, disease prevention, and optimization of harvest decisions. Traditional methods of manually catching and weighing crabs are time-consuming, labor-intensive, and can cause stress or injury to the crabs, while also failing to provide real-time monitoring. To address the challenges posed by turbid water conditions in aquaculture, which lead to poor image quality and difficulty in feature extraction, a method is proposed for estimating Chinese mitten crab weight that combines binocular vision with deep learning–based keypoint detection. This approach achieves high-precision detection of anatomical keypoints on the crab, providing new technical support for precision aquaculture and intelligent management. [Methods] Based on a lightweight YOLOv11 framework, in its C3K2 module, MBConv depthwise-separable convolutions were incorporated to significantly reduce computational complexity and improve feature extraction efficiency. An EffectiveSE channel attention mechanism was introduced to adaptively emphasize important channel-wise features. To further enhance cross-scale information fusion, a spatial dynamic feature fusion module (SDFM) was added. The SDFM adaptively and weightedly fused local spatial attention with global channel attention, enabling detailed extraction of crab shell edges and anatomical keypoints. The improved YOLOv11-ES model could simultaneously output the crab's bounding box, the positions of four anatomical keypoints, and the crab's sex classification in a single forward pass. In the 3D reconstruction stage, calibrated stereo camera parameters were used, and a sparse keypoint matching strategy guided by the crab's sex and spatial geometric constraints was employed. High-confidence keypoint pairs were selected from the left and right views, and the true 3D coordinates of the crab's carapace length and width were computed by triangulation. Finally, the obtained carapace length, width, and sex label data were fed into a two-layer back-propagation (BP) neural network to perform a regression prediction of the individual crab's weight. [Results and Discussion] To validate the effectiveness and robustness of the proposed method, a dataset of Chinese mitten crab images with annotated keypoints was constructed under varying water turbidity and lighting conditions, and both ablation and comparative experiments were conducted. The YOLOv11-ES achieved a mean average precision at intersection over union (IOU) threshold of 0.5 (mAP@50) of 97.2% on the test set, which was 4.4 percentage points higher than the original YOLOv11 model. The keypoint detection component reached an mAP@50 of 96.7%, which was 3.6 percentage points higher than that of the original YOLOv11 model. In comparative experiments, YOLOv11-ES also demonstrated significant advantages over other models in the same series. Moreover, in a full-system evaluation using images of 30 individual crabs, the mean absolute percentage error (MAPE) for carapace width measurements was only 2.68%, and for carapace length it was 1.48%. The Pearson correlation coefficients between the measured and manually obtained true values for both carapace length and width exceeded 0.977, indicating high accuracy in the 3D reconstruction and minimal measurement error. Experiments analyzing the influence of image quality on measurement accuracy showed that when the underwater image quality measure (UIQM) reached at least 1.5, the combined MAPE of carapace length and width errors could be kept below 5%. When UIQM reached at least 2.2, the MAPE dropped to about 1.9%. These results confirmed the robustness of the method against variations in water turbidity and lighting conditions. For weight regression prediction, the BP network trained on carapace length, width, and sex features achieved a mean absolute error (MAE) of 2.39 g and a MAPE of 7.1% on an independent test set, demonstrating high-precision estimation of individual crab weight. [Conclusions] The proposed method, which combines an improved YOLOv11 object detection network, binocular sparse keypoint matching, and a two-layer BP regression network, enabled high-precision, low-error, real-time, non-contact estimation of Chinese mitten crab weight in complex turbid aquatic environments. This approach featured a lightweight model, high computational efficiency, excellent measurement accuracy, and strong adaptability to varying environmental conditions. It provided key technical parameters for intelligent Chinese mitten crab farming. In the future, this approach could be extended to other aquaculture species and complex farming scenarios. Combined with transfer learning and online adaptive calibration techniques, its generalization capability could be further improved and integrated with intelligent monitoring platforms to achieve large-scale, all-weather underwater crab weight estimation, contributing to the sustainable development of smart aquaculture.
[Objective] Trajectory tracking and obstacle avoidance control are important components of autonomous driving chassis, but most current studies treat these two issues as two independent tasks, which will cause the chassis to stop trajectory tracking when facing an obstacle, and then implement trajectory tracking again after completing obstacle avoidance. If the distance from the reference path after obstacle avoidance is too far, the subsequent tracking performance will be affected. There are also some studies on trajectory tracking and obstacle avoidance at the same time, but these studies are either not smooth enough and prone to chatter, or the control system is too complex. Therefore, a simple algorithm is proposed that can simultaneously implement trajectory tracking and obstacle avoidance control of the chassis in this research. [Methods] First, the kinematic model and kinematic error model of the chassis were designed. Since skid-steering was adopted, the kinematic model of the chassis was simplified to a two-wheel differential rotation robot model when designing the mathematical model. Secondly, the Takagi-Sugeno (T-S) fuzzy controller of the chassis was designed. Since the error model of the chassis was designed in advance, the T-S fuzzy model of the chassis could be designed. Based on the T-S model, a T-S fuzzy controller was designed using the parallel distributed compensation (PDC) algorithm. The linear quadratic regulator (LQR) controller was used as the state feedback controller of each fuzzy subsystem in the T-S fuzzy controller to form a global T-S fuzzy controller, which could realize the trajectory tracking function of the chassis when there were no obstacles. Secondly, the obstacle avoidance controller of the chassis was designed. A new controller was designed in the global open-loop system to generate the reference trajectory to avoid obstacles. When the system detects an obstacle in the environment, the controller starts working, and generates a new path by judging the distance between the obstacle and the chassis, so that the chassis could avoid the obstacle. When the chassis bypassed the obstacle, the controller stopped working. In order to better realize the obstacle avoidance function, a fuzzy controller was designed to adjust the gain matrices Q and R of the controller in real time. Then, in order to realize trajectory tracking and obstacle avoidance controlled at the same time, a fuzzy fusion controller was designed to combine the two controllers to form the final chassis input, and the Mamdani fuzzy controller was selected to achieve it. Finally, the method was simulated and experimental tested. The simulation test used joint simulation test used MATLAB-Simulink and the experiments based on the self-developed electric multi-functional chassis were conducted. [Results and Discussions] The simulation results showed that when there were no obstacles, the control method could achieve stable trajectory tracking in the reference path composed of straight lines and curves. When there were obstacles, the vehicle could avoid them smoothly and quickly converge to the reference trajectory. When facing obstacles, the designed fuzzy logic controller could adaptively change the controller gain matrix according to the vehicle's speed and the distance between the current obstacles to achieve rapid convergence. The experimental results showed that when there were no obstacles, the chassis could use the T-S fuzzy controller to achieve stable tracking of the reference trajectory, and the average errors in the lateral and longitudinal directions of the entire tracking process were 0.041 and 0.052 m, respectively. When facing obstacles, the T-S fuzzy controller and the controller realized the obstacle avoidance and tracking control of the chassis through joint control. The fuzzy controller was used to adjust the gain matrix of the controller in real time, and the tracking error was reduced by 33.9% compared with the controller with a fixed gain matrix. [Conclusions] The control system can simultaneously realize the trajectory tracking and obstacle avoidance control of the chassis, can quickly converge the tracking error to zero, and achieve smooth obstacle avoidance control. Although the control method proposed is simple and efficient, and the tracking and obstacle avoidance effects are significantly improved, the control method can only handle static obstacles on the reference path at present, and subsequent research will focus on dynamic obstacles.
