[Significance] The low-altitude economy in orchards represents a key emerging direction in the integrated development of new-quality productive forces in agriculture. As a burgeoning industry driving the high-quality development of the fruit sector, it relies on the integration of advanced equipment manufacturing, the application of smart agriculture technologies and the expansion of consumer-centric ecosystems. These elements contribute to building a full-cycle industrial chain encompassing orchard production, management and services. This fosters the coordinated development of the entire low-altitude value chain and supports the formation of a closed-loop industrial ecosystem. This paper systematically reviews the key technological pathways and development trends of the orchard low-altitude economy across three dimensions: upstream equipment manufacturing, midstream operational processes and downstream service systems. The aim is to provide strategic reference for technological innovation and industrial planning in related fields. [Progress] In the upstream segment, research and industrial development are increasingly focused on lightweight and multifunctional aerial platforms tailored to the complex terrain of mountainous orchards. By utilizing carbon fiber composites, high energy-density batteries and hybrid power systems, these platforms achieve significant reductions in weight and improvements in flight endurance. The integration of artificial intelligence (AI) computing chips, light detection and ranging (LiDAR) and multispectral sensors equips drones with advanced capabilities for precise fruit tree recognition, obstacle avoidance in complex landscapes and multimodal environmental perception. With centimeter-level real-time kinematic (RTK) positioning and multi-sensor fusion flight control algorithms, operational safety and autonomy have been greatly enhanced. Furthermore, low-altitude infrastructure, such as distributed takeoff and landing points and mobile battery-swapping stations, based on integrated 5G-Advanced and BeiDou navigation communication systems, is being systematically deployed. This provides strong support for continuous unmanned operations in hilly and mountainous orchards. The midstream segment, encompassing the pre-production, in-production and post-production stages, serves as the core scenario for value realization in the low-altitude economy. In the pre-production stage, high-resolution remote sensing imagery, combined with machine learning models such as extreme gradient boosting (XGBoost) and convolutional neural networks, enables detailed diagnostics of soil nutrients, micro-topography and vegetation cover. These insights support the precise planning of digital orchards. During the in-production stage, monitoring models based on indices such as normalized difference vegetation index (NDVI) and leaf area index (LAI) facilitate real-time assessment of tree vigor and early detection of pests and diseases, enhancing the management of plant health and growth conditions. Intelligent systems that integrate target recognition, path optimization and electric atomizing nozzles allow for precise, demand-driven application of pesticides and fertilizers, thereby improving resource efficiency and reducing environmental impact. Additionally, collaborative multi-UAV (unmanned aerial vehicle) operations and ground-aerial collaboration, optimized through genetic algorithms and digital twin models, further enhance task scheduling, flight path planning and energy utilization. In the post-production stage, drones equipped with robotic arms or vacuum suction grippers, coupled with thermal imaging, are increasingly effective in fruit identification and targeted harvesting, achieving higher levels of automation and reliability. At the same time, low-altitude logistics networks, supported by autonomous navigation and multi-sensor obstacle avoidance technologies, are addressing the last-mile challenges in cold-chain transportation. This significantly shortens the time window from field to sorting center, improving overall supply chain efficiency. At the downstream service level, the orchard low-altitude economy has evolved beyond single-equipment sales into a diversified service ecosystem. This emerging model centers on pilot training, drone insurance, equipment leasing and the integration of orchard tourism, forming a new type of business landscape. On one hand, standardized pilot training programs and operational quality evaluation systems have enhanced both talent development and safety assurance. On the other hand, risk control models developed by insurers based on operational data, along with "rent-to-own" financing schemes, have effectively lowered entry barriers for farmers. Moreover, the rise of integrated low-altitude agri-tourism models is steadily boosting the brand value of fruit products and generating new income streams through cultural and tourism-related activities. [Conclusions and Prospects] As a vital carrier of new-quality productive forces in agriculture, the orchard low-altitude economy has established a comprehensive industrial chain encompassing equipment manufacturing, operational systems and service platforms. This integrated structure is driving the transformation of orchard management toward greater intelligence, precision and sustainability. Despite current challenges such as limited equipment endurance and underdeveloped service systems, the sector is expected to achieve continuous breakthroughs through the development of high-payload aerial platforms, the integration of data-driven operational systems, the construction of diversified service ecosystems, and the refinement of relevant policies and standards. With the gradual opening of low-altitude airspace and the rapid iteration of core technologies, the orchard low-altitude economy is poised to become a key driver of agricultural modernization and rural revitalization.
[Objective] The accurate identification of maize tassels is critical for the production of hybrid seed. Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity, insufficient feature extraction, high computational load, and low detection efficiency. To address these challenges, a real-time field maize tassel detection model, LightTassel-YOLO (You Only Look Once) based on an improved YOLOv11n is proposed. The model is designed to quickly and accurately identify maize tassels, enabling efficient operation of detasseling unmanned aerial vehicles (UAVs) and reducing the impact of manual intervention. [Methods] Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs, establishing a large-scale, high-quality maize tassel dataset that covered different maize tasseling stages, multiple varieties, varying altitudes, and diverse meteorological conditions. First, EfficientViT (Efficient vision transformer) was applied as the backbone network to enhance the ability to perceive information across multi-scale features. Second, the C2PSA-CPCA (Convolutional block with parallel spatial attention with channel prior convolutional attention) module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps, effectively enhancing the network's capability to extract target features while reducing computational complexity. Finally, the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction, thereby improving the model's detection accuracy. [Results and Discussions] The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection. The final model achieved an accuracy of 92.6%, a recall of 89.1%, and an AP@0.5 of 94.7%, representing improvements of 2.5, 3.8 and 4.0 percentage points over the baseline model YOLOv11n, respectively. The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs. In addition, LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN, SSD, and multiple versions of the YOLO series. The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios. [Conclusions] The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.
[Objective] The detection of corn borer infestations is essential for improving corn yield and quality, as corn borer pests pose a significant threat to global corn production. In traditional agricultural practices, identifying corn borer infestations relies on manual field inspections or trapping tools, which are labor-intensive, time-consuming, and difficult to implement over large areas. These methods are further limited by their susceptibility to human error and inability to meet the demands of modern precision agriculture. To address these challenges, a method for detecting corn borer infestations using low-altitude, close-range imagery captured by unmanned aerial vehicles (UAVs) was investigated. By focusing on detecting boreholes rather than insect bodies, this approach overcomes the difficulties of detecting corn borers, which are nocturnal and often concealed within plant tissues, thereby enhancing the applicability of field-based detection and aligning with practical field conditions. [Methods] Based on the YOLOv11 (You Only Look Once v11) object detection algorithm, a model named YOLO-ESN was introduced, for corn borer infestation detection. The YOLO-ESN model was optimized through multiple modifications. In the Backbone, an enhanced lightweight attention (ELA) mechanism was incorporated to increase sensitivity and improve the extraction of small visual features, such as boreholes, by modeling spatial dependencies in horizontal and vertical directions using one-dimensional convolutions. In the Neck, a C3k2-Spatial and channel reconstruction convolution (C3k2-SCConv) module was introduced to reduce the number of model parameters while improving feature fusion efficiency through spatial and channel reconstruction, suppressing redundant information. In the Head, a small-object detection branch, termed the P2 detection head, was added, enabling YOLO-ESN to directly utilize shallow, high-resolution features from early network layers to enhance the detection of fine-grained targets like boreholes. Additionally, a combined loss function of normalized Wasserstein distance (NWD) and efficient intersection over union (EIoU) was employed to optimize bounding box regression accuracy, addressing gradient vanishing issues for small targets and improving target localization stability and robustness. A decision tree algorithm was applied to classify infestation severity levels based on borehole detection results, and heatmaps were generated to visualize the spatial distribution of corn borer infestations across the field. [Results and Discussions] Multiple experiments were conducted using a constructed dataset of corn borer infestation images. The results demonstrated that YOLO-ESN achieved an mAP@50 of 88.6% and an mAP@50:95 of 40.5%, representing an improvement of 7.6 and 4.9 percentage points, respectively, compared to the original YOLOv11 model. The total number of parameters in YOLO-ESN was reduced by 11.52%, contributing to a lighter model suitable for UAV deployment. Ablation studies evaluated individual contributions: incorporating the ELA mechanism alone improved mAP@50 by 0.3 percentage points, and the parameters are reduced by 10.57%; replacing the C3k2 module with C3k2-SCConv reduced parameters by 2.5% while increasing mAP@50 by 0.9 percentage points; adding the P2 detection head enhanced mAP@50 and mAP@50:95 by 4.1 and 1.2 percentage points, respectively; and introducing the NWD+EIoU loss function improved mAP@50 and mAP@50:95 by 1.9 and 1.2 percentage points, respectively. Comparative experiments demonstrate that YOLO-ESN outperforms a range of mainstream object detection models, including Faster R-CNN, SSD, YOLOv8, YOLOv11, and YOLOv12. YOLO-ESN achieves an mAP@50 and an mAP@50:95, surpassing Faster R-CNN by 14.9 and 9.7 percentage points, respectively, and SSD by 17.8 and 11.4 percentage points, respectively. With a compact parameter size of 8.37 M, YOLO-ESN delivers excellent detection accuracy and generalization, striking a strong balance between performance and efficiency. Although its inference speed (32.48 frame/s) was slightly slower than YOLOv12 (75.44 frame/s), it offered a superior trade-off between accuracy and efficiency. These results validated YOLO-ESN as a lightweight, high-performing solution for small object detection tasks, such as dense small targets in remote sensing images. The decision tree algorithm classified infestation severity with high accuracy, achieving F1-Scores of 0.906, 0.803, and 0.842 for mild, moderate, and severe infestations, respectively. Heatmaps generated from borehole detection results enabled spatial visualization of infestation severity, providing a scientific basis for quantitative monitoring and targeted pesticide application in field infestations. [Conclusions] The proposed YOLO-ESN model has more advantages in overall detection accuracy and running speed. While improving the lightweight degree and deployment efficiency of the model, it also shows better recognition ability in small target detection, and can accurately locate the wormhole area on the corn leaf, effectively improving the bounding box regression accuracy and feature extraction efficiency. Compared with the traditional insect recognition method, the use of wormholes as detection objects is more in line with the actual field situation, effectively avoiding the problems of insect occlusion and strong concealment, and improving the availability of field image data and algorithm robustness. The heat map generated by the model detection results can also effectively display the distribution changes of insect pests in farmland, providing a scientific basis for precision pesticide spraying and farmland management. Overall, this study provides an effective solution for the intelligent detection of corn borer pests, has strong versatility and promotion prospects, and can provide strong technical support for precision agriculture and smart farmland management.
