As a core carrier of the low-altitude economy in the agricultural and rural sectors, agricultural unmanned aircraft have experienced explosive growth in recent years and have become a vital technological force driving agricultural modernization and rural revitalization. While the industrial scale has expanded dramatically and technological applications have undergone rapid iteration, the development of supporting regulations and standards has lagged behind. Based on a review of the development of domestic and international regulations and standards for agricultural unmanned aerial vehicles, this paper systematically analyzes the legal framework for airspace management, airworthiness certification, and personnel qualifications-as well as the current status of the four-tier standards framework including national standards, industry standards, local standards, and group standards. The findings indicate that China has initially established a regulatory framework with the Interim Regulation on the Flight Management of Unmanned Aircraft as the overarching legislation, and a standards architecture with the Guidelines for the Construction of the Low-Altitude Economy Standards System as the top-level design. However, shortcomings remain in areas such as application scenario coverage, component and subsystem requirements, and supporting methodological standards. Looking into the future, efforts should be directed toward advancing risk-based classification and hierarchical management, accelerating the expansion of application scenarios and the refinement of methods within the standards system, and building a digital intelligent supervision system, thereby providing institutional safeguards for the high-quality development of the agricultural low-altitude economy.

Accurate quantitative prediction of crop yield is a crucial foundation for ensuring national food security and formulating smart agricultural decisions. During the rice growth cycle, the full heading stage is the most critical period for biomass accumulation, and its canopy spectral characteristics contain rich information on biochemical parameters and yield potential. This study explores the application advantages of combining multi-dimensional UAV remote sensing features with advanced machine learning algorithms for rice yield prediction at the full heading stage. Using a multispectral UAV to acquire images, this research systematically constructed six categories of feature sets, including red-green-blue (RGB), multispectral (MS), multispectral to hyperspectral (MSTPP), spatial texture (Texture), vegetation index (VI), and multi-dimensional fusion (Fusion). At the algorithmic level, eight machine learning algorithms were comprehensively evaluated, with a focus on comparing kernel methods represented by support vector regression (SVR) and various nonlinear ensemble models. The experimental results indicate that MS data exhibit outstanding prediction potential among single-dimensional features. The strategy of fusing spectral and texture information achieved excellent prediction stability, wherein the SVR model combined with multi-dimensional fusion features achieved an ideal fitting effect (coefficient of determination R²=0.828), calibrating the spectral signals impaired by canopy closure during the full heading stage, and effectively correcting the systematic estimation bias of single-dimensional models in extreme yield intervals. Furthermore, this experiment verified through the introduction of the Shapley additive explanations (SHAP) framework that the green normalized difference vegetation index (GNDVI) played a core driving role in yield prediction, objectively revealing the safeguard mechanism of population growth balance on the final yield. The approach of "multi-dimensional fusion combined with nonlinear algorithms and attribution analysis" explored in this study provides a feasible approach for constructing quantitative yield estimation models with both high robustness and agronomic interpretability.

The development of the low-altitude economy is reshaping the technological functions, organizational forms, and regulatory demands of agricultural aircraft. Represented by agricultural drones, such aircraft have evolved from seasonal plant protection equipment into low-altitude production units integrating field operations, crop and farmland sensing, disaster response, and socialized service provision. As application boundaries continue to expand, the risks associated with agricultural low-altitude operations have also shifted from conventional flight safety concerns to a more comprehensive governance problem in which flight safety, operational quality, environmental externalities, and liability traceability are mutually intertwined. Against this background, a comparative analytical framework was developed in this study centered on airspace accessibility, operational authorization, regulatory visibility, operational compliance, and enforcement traceability. And the governance regimes for agricultural low-altitude operations across major jurisdictions, including China, the United States, the European Union, Australia and New Zealand, were systematically reviewed. In addition, a regulatory release density indicator was introduced, together with publicly available data on registered operators, registered unmanned aircraft, and remote pilots, to enable comparison of the relationships between diverse regulatory supply forms and large-scale operational capacity. The findings showed that major jurisdictions have developed three broad governance pathways: a strong-foundation model, a high-efficiency authorization model, and a risk-assessment-and-service model. In agricultural scenarios, institutional effectiveness depends not on the sheer number of individual rules, but on whether airspace access, digital identification, operational oversight, and evidentiary record retention can be integrated into an executable and verifiable engineering closed loop. Moreover, the concurrent expansion of common application scenarios, including production operations, remote sensing monitoring, emergency support, and cross regional socialized services, has further intensified the demand for differentiated authorization, tiered regulation of payload release, and cross regional collaborative oversight. On this basis, the study proposed an engineering-oriented compliance pathway centered on mission-based classification, the coupling of authorization and traceable records, tiered control of payload release, and agriculture-oriented continuing airworthiness management. The results may provide a useful reference for improving Chinese governance system of the agricultural low-altitude economy and for promoting the standardized application of agricultural drones.

The deep integration of the low-altitude economy with agricultural and rural development is of strategic importance for enhancing comprehensive competitiveness, building a modern agricultural production system, and strengthening national food security. This study systematically reviews the application scenarios and existing challenges of low-altitude aircraft in the hilly and mountainous regions of Sichuan Province. It elaborates on the various low-altitude carrier platforms and their supporting power systems, as well as the intelligent control and management technology systems deployed in these regions. The study analyzes application scenarios including intelligent plant protection and precision pesticide application, low-altitude remote sensing and agricultural condition monitoring, transportation of agricultural inputs and products, seeding, fertilization, and pollination operations, as well as emergency support and logistics distribution. Furthermore, it identifies the issues and challenges faced by low-altitude carrier platforms in the hilly regions across three key aspects: equipment R&D and manufacturing, intelligent operational technologies, and operation and maintenance. From the perspective of critical technology breakthroughs, it proposes conducting integrated validation of models for full-process mechanized and intelligent systems alongside complete equipment sets. It also suggests developing a testing and validation method for “air-space-ground” integrated collaborative operation system for agricultural machinery in hilly regions. From the three dimensions of intelligence, greenness, and safety, it further clarifies the development requirements for low-altitude agricultural aviation equipment technology. From the perspective of optimizing the innovation ecosystem, it proposes improving mechanisms for talent introduction and cultivation, establishing a “platform support-talent guarantee-industry collaboration” trinity industry-university-research development system, and forming a virtuous-cycle innovation system for the low-altitude agricultural equipment industry. This approach aims to comprehensively enhance the technological level and industrial competitiveness of agricultural machinery in hilly regions. By establishing full-process mechanization demonstration zones in typical hilly regions such as Sichuan Province, conducting equipment validation and technology maturation, and strengthening training for grassroots operators, it provides directional guidance for developing replicable and scalable integrated solutions for agricultural mechanization in hilly regions.

As the low-altitude economy joins the ranks of national strategic emerging industries, the integration of low-altitude agricultural technology and equipment into agricultural production continues to deepen, serving as a cornerstone for promoting agricultural modernization and intelligent agriculture. This paper systematically evaluates the development status and future trends of low-altitude agricultural technology and equipment in China, focusing on policy frameworks, key technologies, and typical application scenarios. First, from the perspective of aerial platforms and intelligent control, the paper summarizes the current development of multi-rotor, single-rotor, and hybrid unmanned aerial vehicles (UAVs), and traces the evolution of key technologies in power systems, flight control algorithms, and navigation positioning. Concurrently, the progress of intelligent control technologies is outlined with an emphasis on low-altitude communication, cloud-based management, and multi-UAV coordination. Second, regarding low-altitude sensing and precision operations, the paper highlights the applications of UAV-based remote sensing in crop monitoring, alongside the technological pathways of precision spraying and broadcast sowing, identifying variable-rate operations and intelligent control as pivotal research frontiers. Finally, by examinging the inspection and evaluation systems and standardization frameworks of low-altitude agricultural equipment, the paper identifies existing challenges in operational specifications, quality evaluation, and systematic standards. The analysis reveals that although low-altitude agricultural technology offers significant advantages in operational efficiency and resource utilization, it remains constrained by insufficient equipment adaptability, limited operational stability in complex environments, inadequate multi-source information fusion and weak intelligent decision-making capabilities, and incomplete standard systems. Future research should prioritize multi-source sensing fusion, intelligent control, and data-driven decision-making technologies, while improving standard and evaluation systems, to promote the development of low-altitude agricultural technology toward precision, intelligence, and large-scale applications. This study provides a reference for research, equipment development, and policymaking in the field of low-altitude agriculture.

To address the degradation of power generation efficiency caused by soiling accumulation in photovoltaic (PV) power plants, as well as the localization challenges faced by cleaning unmanned aerial vehicles (UAVs) due to limitations in edge computing power and complex background interference, this paper proposes an edge-computed cleaning UAV solution based on the Jetson Orin Nano platform. Within the YOLO11n framework, a lightweight photovoltaic panel soiling detection algorithm designated as GOP-YOLO11n is developed. By introducing GroupDC structure, the C3k2 modules in the backbone network undergo lightweight reconstruction, minimizing computational overhead while enhancing the feature representation of small targets against complex backgrounds. Furthermore, an OptimizedDC multi-scale feature extraction module is embedded into the feature fusion network to improve the perception of multi-scale targets, such as tiny bird droppings and elongated water stains. In the detection head, an upsampling and feature aggregation block (UP-Block) is implemented to strengthen multi-pixel pattern aggregation. For the experiments, a visible-light image dataset of PV panels captured by UAVs, yielding 4 190 valid imagespartitioned into training, validation, and test sets at an 8：1：1 ratio, with input resolution standardized to 960 pixels × 960 pixels. Under identical training and testing configurations, GOP-YOLO11n achieved a Precision of 91.1%, a Recall of 85.8%, a mAP@50 of 92.5%, and a mAP@50：0.95 of 76.8%, outperforming the baseline YOLO11n model by 2.9, 3.4, 1.8, and 4.4 percentage points, respectively. The model parameters were reduced from 2.582 M to 2.068 M, with a floating-point operation count of 6.7×10⁹. Moreover, hardware deployment tests using TensorRT FP16 on the Jetson Orin Nano Super 8 G platform showed an inference speed of 114.7 f/s, a single-frame latency of 8.7 ms, and a GPU memory footprint of 1 685 MB. The results indicate that the proposed method effectively reconciles the need for high-accuracy multi-scale soiling detection in complex scenes with the constraints of lightweight edge-side real-time inference, thereby providing robust visual perception support for soiling recognition, localized cleaning strategies, and the intelligent operation and maintenance (O&M) of PV cleaning UAVs.

Unmanned aerial vehicle (UAV)-based fertilization is increasingly applied in rice production; however, fertilization prescriptions are still largely dependent on empirical knowledge and lack decision support based on real-time crop nutrient status. Meanwhile, limitations in hyperspectral data processing and model construction restrict the high-precision estimation of chlorophyll content in cold-region rice, thereby constraining the accurate implementation of variable-rate fertilization. To address these issues, this study combined multiple spectral preprocessing methods with feature variable selection techniques, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), iterative retention of informative variables (IRIV), and variable iterative space shrinkage approach (VISSA), to extract key spectral bands. Models including least squares support vector regression (LSSVR), particle swarm optimization-based LSSVR (PSO-LSSVR), partial least squares regression (PLSR), and a deep learning model (1DCNN-WN) were developed for chlorophyll content inversion. Based on the inversion results, nitrogen fertilization prescription maps were generated to guide UAV-based variable-rate fertilization at the tillering stage. The results showed that the SNV-IRIV-1DCNN-WN model achieved the highest inversion accuracy under the optimal preprocessing and feature selection combinations, with an R² of 0.687, which was 0.047, 0.031, and 0.027 higher than those of SNV-UVE-LSSVR, NL-VISSA-PSO-LSSVR, and MSC-IRIV-PLSR, respectively, while the RMSE was reduced by 0.281, 0.147, and 0.107. The coefficients of variation for no fertilization (N0), low fertilization (N1), standard fertilization (N2), and excessive fertilization (N3) treatments were 6.08%, 4.16%, 3.42%, and 3.07%, respectively, indicating good fertilization uniformity. These findings provide theoretical support and technical guidance for hyperspectral-based chlorophyll inversion and precision fertilization in cold-region rice production.

The leaf area index (LAI) is a key biophysical parameter characterizing the canopy structure and growth status of winter wheat, playing a crucial role in precision agriculture monitoring and crop condition assessment. However, existing inversion methods suffer from unstable accuracy and insufficient model generalization capabilities across varying flight altitudes. To adress these limitaions, this study develops an LAI estimation framework based on multi-altitude UAV multispectral data by integrating ensemble learning architectures with multiple machine learning algorithms. Analysis of variance (ANOVA) was applied to analyze the mechanisms through which flight altitude influences spectral reflectance and textural features. Following the extraction of vegetation and textural indices from the multispectral data, Pearson and Spearman correlation analyses were conducted to screen LAI-sensitive features for predictive modeling. Four distinct models—XGBoost, RF, XGBoost-EN, and RF-Ridge—were subsequently established to perform multi-altitude and multi-feature LAI inversions and comparative accuracy evaluations. The results indicate that UAV flight altitude exerts a significant scale effect on both reflectance and textural characteristics, with 80 m indentified as the critical threshold for reflectance variations. Regardless of whether single-feature or mixed-feature inputs were utilized, 20 m flight altitude consistently yielded the highest estimation accuracy across all tested altitudes, R² achieving a maximum of 0.745, RMSE of 0.699, and MAE of 0.507. Furthermore, The XGB-EN and RF-Ridge ensemble models outperformed the standalone machine learning models, exhibiting superior inversion performance with an R² of up to 0.802, an RMSE down to 0.592, and an MAE down to 0.421, indicating excellent fitting accuracy and stability. Overall, XGBoost-EN is the optimal model for the LAI inversion task. This study validated the feasibility of ensemble learning models and machine learning methods for high-precision LAI inversion across multiple altitudes, providing a theoretical foundation and technical pathway for remote sensing-driven crop growth monitoring.

Utilizing radiative transfer models to simulate spectral data for improving the robustness and interpretability of leaf area index (LAI) inversion models is a major focus in current research. However, the application of point cloud data, which can directly reflect the physical structure of rice, remains underdeveloped. To bridge this gap, this study proposes a rice LAI inversion method that integrates a three-dimensional radiative transfer model with unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology. Virtual rice canopy scenes were constructed using the LESS three-dimensional radiative transfer model to generate simulated point cloud data, while UAV LiDAR was employed to collect field-measured data. Pearson correlation analysis was used to select key features, leading to the construction of a feature subset comprising height features, vertical layer density features, and cumulative density features. Three machine learning models-ridge regression, support vector regression, and random forest-were trained and validated on simulated datasets, measured datasets, and a simulation-measurement hybrid dataset. The results show that the simulated data generated by the LESS model demonstrate strong consistency with measured point clouds in vertical distribution, confirming the validity of the simulation. Feature analysis indicates that the 90th percentile height (H90) and vegetation canopy volume (VCM) manifest the strongest correlations with LAI. Model comparison reveals that the random forest model performs best on the hybrid dataset, achieving a validation R² of 0.918 and RMSE of 0.787. The hybrid dataset strategy yields better fitting performance than using the measured dataset alone, effectively enhancing model generalization. In conclusion, the proposed method, which integrats three-dimensional radiative transfer modeling and UAV LiDAR technology, enables rapid and accurate estimation of rice LAI, providing a new technical pathway for precise monitoring of rice growth status and supporting high-throughput acquisition of crop phenotypic information within the framework of agricultural low-altitude economy.

Real-time and accurate monitoring of the winter wheat nitrogen nutrition index (NNI) is of great significance for optimizing field water and fertilizer management and reducing agricultural non-point source pollution. To address the issues of modal confusion in existing deep networks when processing multi-source UAV remote sensing data, as well as the loss of two-dimensional spatial topological information during temporal modeling, this study proposes a dual-branch multi-source feature fusion memory network (DMFM-Net). Multispectral images and canopy structural data of winter wheat at the tillering, jointing, and booting stages were collected by UAV. An enhanced CBAM-ResNet50 dual-branch backbone was employed to separately extract spectral features and canopy structural features. The spatially preserved feature tensors were then directly input into a convolutional long short-term memory (ConvLSTM) network for temporal modeling. The results show that: (1) Compared with the traditional serial network (CBAM-ResNet-LSTM) and the single-branch network (CBAM-ResNet-ConvLSTM), DMFM-Net effectively overcomes the noise interference of multimodal heterogeneous data and achieves the best inversion performance at all key growth stages. The coefficients of determination (R²) of the model at the tillering, jointing, and booting stages reach 0.76, 0.86, and 0.82, respectively, with root mean square errors (RMSE) as low as 0.11, 0.10, and 0.14. (2) Ablation experiments further confirm the necessity and scientific validity of the proposed architectural design. The ConvLSTM model lacking a deep spatial encoder shows a significant decrease in R² of 0.21-0.24. Moreover, compared with the static spatiotemporal feature extraction of 3D-ResNet, DMFM-Net, by virtue of its ConvLSTM module covering a long-range dynamic memory mechanism across the whole growth period, improves the R² of the inversion models at each stage by 0.07-0.16. (3) The field-scale spatial distribution maps generated by the optimal model accurately reconstruct the local spatial heterogeneity and temporal evolution patterns of winter wheat nitrogen nutritional status under different nitrogen application gradients. In summary, this study provides a new theoretical paradigm and technical support for multimodal time-series dynamic monitoring of crop nutritional status in complex field environments.

Existing agricultural monitoring platforms in open-field environments generally suffer from limited endurance of unmanned aerial vehicles, insufficient viewpoint consistency during low-altitude repeated observations, and high susceptibility of ground robots to surface conditions and crop canopy occlusion, making it difficult to concurrently meet the demands of rapid deployment and highly repeatable observation. To achieve continuous acquisition and comparative analysis of agricultural information at the same location and from the same viewpoint over different periods, a wheel-wing hybrid cable-travelling inspection robot for agricultural monitoring was designed. Using overhead cables above farmland as fixed inspection paths, a dual-mode inspection scheme combining aerial cable docking and cable-travelling locomotion was proposed to meet the demand for repeated observation at fixed locations and viewpoints. On this basis, an integrated configuration composed of a passive trigger gripper, deformable arms, and wheel-wing hybrid end-effectors was developed to realize structural reuse of the aerial execution unit and the cable-travelling execution unit. In addition, key constraint models, including flight hovering, anti-slip on the cable, cable-travelling driving torque, and arm driving with over-center self-locking, were established, and SolidWorks and ADAMS were used to simulate mode switching and cable-travelling operation. The simulation results showed that the two arms of the robot could fold synchronously and the mode-switching process was continuous and stable. The robot could complete the transition from the flight-deployed mode to the cable-travelling working mode within approximately 5 seconds. In the cable-travelling mode, the platform moved continuously with stable wheel-cable contact, and the pitch angle variation of the front and rear wheels ranged from about −0.2 to 0.2 rad, indicating favorable path constraint and observation posture stability. The proposed robot can satisfy both rapid access to field monitoring areas and stable fixed-path inspection requirements, providing a reference for temporal agricultural monitoring, repeated observation data acquisition, and the development of low-cost agricultural inspection equipment.

Oil-powered plant protection unmanned helicopters play an indispensable role in modern precision agriculture due to their large payload and long endurance. However, their complex operational mechanisms, including broadband strong vibration, high-temperature engine radiation, and coupling among multiple subsystems, impose extremely high demands on condition monitoring. Existing monitoring methods mostly rely on closed flight control systems, which commonly suffer from issues such as missing on-board equipment status data, low telemetry data update rates, high transmission latency, and non-open interfaces, leading to severe deficiencies in real-time flight status monitoring and fault diagnosis capabilities. To address these problems, a multi-signal acquisition and condition monitoring system is designed that is completely independent of the original flight control system. At the hardware circuit level, by integrating an MPU6050 attitude sensor, laser rangefinder, thermocouples, and optoelectronic isolation detection circuits, the system achieves high-frequency acquisition of nine types of key parameters, including flight attitude angles, engine speed, exhaust gas temperature, generator three-phase voltage, and servo command/feedback signals (attitude at 5 Hz, status at 1 Hz). The extended MavLink protocol is used for data encapsulation, and data are transmitted to a ground station via 4G/5G networks in a time-sharing manner, combined with on-board cyclic data storage, thereby establishing a dual-redundant data link. On this basis, an engine overheating early-warning method based on a temperature-speed coupling model and a fault detection algorithm for servo actuators based on an Extended Kalman Filter (EKF) are proposed. Test results show that the system operates independently and stably, reducing the uplink latency of critical attitude data to less than 200 ms, and successfully achieving rapid identification of faults such as generator phase loss (response <1 s) and servo jamming. This system can serve as an independent on-board “black box”, providing a reliable data foundation and diagnostic tool for real-time condition monitoring, predictive maintenance, and post-flight incident analysis of oil-powered unmanned helicopters.

In the transition from traditional agriculture to smart agriculture, the demand for large-scale, high-precision information perception in agricultural scenarios is increasing. Traditional monocular and binocular vision, limited by their field of view, struggle to meet the requirements of all-round monitoring in complex environments. Panoramic vision technology, characterized by 360° unobstructed field of view, fast and complete information acquisition, and strong real-time performance, has become an irreplaceable key technical means in the intelligent upgrading of agriculture. This paper aims to systematically review the application status and development progress of panoramic vision systems in the agricultural field, providing theoretical reference for the in-depth integration of panoramic vision technology and agricultural equipment. Based on imaging principles and hardware structures, the systems are classified into four architectural types: pan-tilt rotating type, multi-camera array type, fisheye lens type, and catadioptric type. Combined with typical cases, the application evolution in fields such as crop monitoring, phenotypic investigation, and agricultural machinery navigation is analyzed. The research shows that panoramic vision has formed a multi-level application pattern: pan-tilt rotating systems have small distortion but poor real-time performance, suitable for static growth monitoring; multi-camera systems have high frame rates and small pose errors, capable of supporting dynamic high-dimensional phenotypic reconstruction and positioning; fisheye systems have a simple structure, and edge distortion can be suppressed through calibration to achieve wide-area monitoring; catadioptric systems are compact with no delay and small deviations in navigation and obstacle avoidance. Currently, panoramic vision in agricultural applications still faces four major issues: bottlenecks in adaptability to complex environments, shortcomings in real-time processing of dynamic scenes, insufficient efficiency and accuracy of 3D information reconstruction, and lack of technical standardization and system compatibility. Therefore, in the future, panoramic vision technology should focus on multi-modal sensor fusion perception, use deep learning-driven adaptive model optimization to improve generalization ability, and deeply collaborate with the agricultural Internet of Things to build an integrated perception-decision-regulation system. Meanwhile, strengthening technology open-sourcing and industry standardization will accelerate the large-scale application of panoramic vision technology in precision agriculture.

To address the limitations of current agricultural image recognition tasks, such as isolated models, weak multi-task synergy, insufficient real-time performance and generalization capability, this study develops an intelligent agricultural image recognition system based on deep learning and multi-task fusion. This system adopts a "device-edge-cloud" three-level collaborative architecture. By constructing a multi-task joint learning framework, the system enables concurrent training and collaborative inference for both crop height measurement and disease recognition. The system fuses the feature extraction capabilities of mobile convolutional neural networks (MobileNet) and visual transformers (ViT) with a coordinate attention (CA) mechanism; this design enhances key feature extraction, suppresses complex farmland background interference while significantly alleviating computational burden. Experimental results show that the system achieves remarkable performance across both core tasks. For crop height measurement, the determination coefficient R2 between the algorithm′s measurement results and the manual measured values reaches up to 99.98%. For disease recognition, the optimized lightweight model achieves an average detection accuracy of 98.76%, with only 12.64 M parameters. The average single-image inference latency is as low as 9.1 ms, demonstrating excellent potential for embedded deployment. Moreover, the system exhibits high stability in complex environments, maintaining measurement deviation of crop height within ±2 cm. This precision allows for seamless integration into low-altitude monitoring platforms, such as unmanned aerial vehicles (UAVs), to satisfy the real-time data processing requirements of low-altitude agricultural operations. In summary, the proposed system delivers excellent scalability, real-time efficiency, and robustness, effectively overcoming traditional bottlenecks in multi-task synergy, lightweight deployment, and adaptation to complex environments. It provides an integrated solution for multi-dimensional image recognition in smart agriculture empowered by the low-altitude economy, thereby facilitating the large-scale implementation of precision agriculture while improving agricultural production efficiency.

Accurately obtaining annual cropland area and spatiotemporal change information is crucial for safeguarding the agricultural land redline and improving the efficient, economical, and intensive utilization of water resources. It also provides a scientific basis for navigation and intelligent scheduling of agricultural machinery, thereby effectively enhancing the precision and efficiency of mechanized agricultural operations. Taking the Aksu river basin in the Xinjiang Uygur Autonomous Region as an example, this study proposes a method for extracting cropland spatiotemporal change information based on time-series index features. First, based on NDVI data derived from time-series remote sensing images covering the entire crop growth phenological period, annual composite vegetation index images are generated using the maximum value compositing method, and these images are stacked into bands to construct a time-series index feature image. Second, an object-oriented segmentation approach is applied to perform multi-scale segmentation on the feature image to obtain segmentation results. Finally, the change patterns of cropland across different years revealed by the feature image are analyzed, and extraction rules for different cropland change types are established. A decision tree classification method is then employed to achieve rapid extraction of annual cropland spatiotemporal change information. Experimental results demonstrate that the proposed method can extract annual cropland change information rapidly and accurately. Analysis of the annual change information shows that the cropland area in the Aksu river basin exhibited an overall increasing trend from 2021 to 2024, with relatively high growth rates of 1.24% and 1.28% in 2022 and 2023, respectively, while the growth rate slowed to 0.42% in 2024. To validate the accuracy of the extraction results, a comparison between the statistical yearbook data of selected counties and the remote sensing monitoring data was conducted, revealing a relative error of approximately 5%. This confirms that the proposed method attains satisfactory accuracy in extracting cropland area.

Apples, as a high-yield fruit, often suffer significant losses during transportation and storage due to collisions and improper temperature control. Traditional manual inspection methods for apple surface damage are not only costly and inefficient but also heavily reliant on operator experience and subjective factors, leading to limited detection accuracy. To address these issues, this study proposed a cost-effective and efficient apple damage detection method based on laser speckle imaging. The proposed approach integrated the RS_FUJII speckle feature extraction algorithm with the RECONANOM-AE damage detection model to improve detection speed and accuracy. Shandong Fuji apples were used as the experimental samples. Speckle images were first preprocessed, followed by feature extraction. To overcome the limitations of the traditional FUJII algorithm in spatiotemporal feature representation and noise robustness, this study introduces a spatiotemporal weighting mechanism and robustness enhancement strategies to develop an improved RS_FUJII algorithm for high-precision damage feature extraction. In addition, an unsupervised neural network model, RECONANOM-AE, is designed to automatically identify damage regions via reconstruction error. Experimental results demonstrate that the proposed model exhibits favorable convergence and stability during training, with both training and validation errors gradually decreasing with increasing epochs. Comprehensive performance evaluation showed that the model achieved a true positive rate of 86.67% and a false positive rate of 6.67%. The overall recognition Accuracy reached 90%, with a Precision of 92.86%, Recall of 86.67%, Specificity of 93.33%, and an F1 Score of 89.68%. These results demonstrated that the proposed method achieved good training stability and strong generalization ability. The method enabled non-destructive quality assessment of apples and provided a reliable technical solution for intelligent fruit quality inspection.

Accurate localization of keypoints for animal body measurement is of great importance for intelligent livestock health monitoring and automated measurement systems. However, existing keypoint detection models often suffer from two major limitations: a large number of parameters which are unfavorable for deployment on edge devices, and insufficient localization accuracy in some lightweight models that fails to meet the precision requirements of body measurement tasks. To address these challenges, this paper proposes a lightweight keypoint detection model based on an improved YOLOv8-Pose architecture, which effectively enhances detection accuracy while maintaining real-time performance. In the proposed model, coordinate attention mechanisms are integrated into the shallow layers of the backbone network and before the three heads, enabling spatial coordinate information to be effectively embedded into channel attention. This design enhances the model ability to perceive the spatial distribution of keypoints, thereby improving the accuracy and stability of keypoint localization. In addition, a forced sharpening module is introduced in the shallow backbone to strengthen edge and contour features, facilitating a better balance between global semantic representation and local detail preservation. Furthermore, a high-resolution feature branch is incorporated to provide fine-grained spatial information to the three detection heads, compensating for the loss of spatial resolution in deeper feature maps. Experimental results demonstrate that the proposed model achieves a precision of 82.6%, a recall of 81.3%, and an mAP50-95 of 58.1% on complex horse keypoint detection tasks. Compared with YOLOv8-Pose, the proposed method achieves improvements of 21.0%, 39.2%, and 45.3% in precision, recall, and mAP50-95, respectively, exhibiting superior robustness and localization capability under complex poses and diverse environmental conditions. In terms of model complexity, the number of parameters increases from 3.361 M to 4.611 M, corresponding to an increase of approximately 37.2%, while the floating-point operations rises from 9.6×10⁹ to 10.4×10⁹, an increase of only 8.3%. Despite this limited computational overhead, the proposed model delivers substantial performance gains without significantly affecting inference speed. These results indicate that the proposed approach achieves a favorable trade-off between accuracy and efficiency by incurring a relatively small model cost for considerable performance improvement, demonstrating its strong potential for intelligent livestock farming and automated body measurement applications.

To address difficulties and inefficiencies in monitoring duck weight during the routine inspection of cage-raised meat ducks, this study proposes a precise body weight prediction method utilizing a tree-based machine learning algorithm, aiming to enhance the stability and accuracy of weight prediction in complex scenarios of large-scale meat duck production. Unilateral body size and age data were manually collected from meat ducks at Shuyang Xinfeng Future Farm to develop an extreme gradient boosting model predicting weight. The model integrated morphometric features and chronological age as predictors to evaluate their respective impacts on body weight estimation. The results indicate a distinct growth trajectory: for ducks 1-13 days, data collection is hindered by their small body size, and weight growth is relatively slow, making this period less critical for weight prediction modeling. Conversely, ducks aged 14 to 35 days undergo a rapid growth phase, representing a critical period in the production cycle during which age and unilateral body size serve as pivotal predictors. The Extreme Gradient Boosting Tree model effectively predicted the weight of meat ducks, achieving an optimal R² of 0.95, a minimum mean squared error (MSE) of 0.563 kg², and a minimum mean absolute error (MAE) of 0.044 kg. Furthermore, by evaluating the importance of unilateral body size data of meat ducks, it is concluded that body slant length, as the main parameter of meat duck development, contributes the most to the model among body size data, accounting for 20.7% (second only to age) while tibia length exhibited the weakest correlation, contributing only 4.8%. This study not only deepens the understanding of machine learning application in meat duck weight prediction but also provides a valuable method for selection and management in non-contact meat duck weight analysis scenarios. Additionally, it offers technical support for improving model accuracy and promoting the development of intelligent cage-raised meat duck farming, and provides an effective automated solution for weight monitoring via inspection robots.