Abstract:Harvesting robots represent a critical enabling technology for advancing the mechanization and automation of fruit and vegetable harvesting and have become a prominent research focus within the global agricultural robotics community. Unlike structured industrial environments, agricultural harvesting tasks are performed in highly unstructured and dynamic settings, where fruits, branches, and leaves are densely intertwined and frequently occluded one another. As a result, obstacle-aware operation capability has emerged as a key technological bottleneck that fundamentally limits the overall performance, robustness, and practical applicability of fruit and vegetable harvesting robots. In this context, a comprehensive review of recent advances in obstacle-avoidance technologies for harvesting robots operating in complex agricultural environments was provided. The review was structured around four core aspects that were critical to obstacle-aware harvesting: target perception, manipulation decision-making, servo control, and end-effector and execution structures. Firstly, advances in visual and multimodal perception methods for detecting and segmenting fruits, branches, and foliage under occlusion were examined, with particular attention paid to deep learning-based approaches and three-dimensional sensing techniques. Secondly, manipulation decision-making strategies, including motion planning, behavior selection, and learning-based decision models, were reviewed with respect to their ability to cope with high-dimensional constraints and environmental uncertainty. Thirdly, servo control methods for harvesting robots were discussed, focusing on visual servoing, force-aware control, and adaptive strategies that enabled precise and safe manipulation in cluttered scenes. Finally, the design of execution mechanisms and end-effectors was analyzed, highlighting how mechanical structure, compliance, and functional integration influence obstacle avoidance performance during harvesting operations. Based on this review, it was further analyzed and summarized the major technical challenges faced by obstacle-aware harvesting robots in real-world agricultural scenarios. These challenges included limited perception reliability under severe occlusion, insufficient generalization of decision and control strategies across varying crop types and growth stages, difficulties in reproducing human harvesting skills, and the lack of coordination between robotic system design and agricultural production practices. Finally, future research trends were discussed, emphasizing the potential of embodied intelligence, end-to-end learning frameworks, and the integration of agronomic knowledge with robotic design. With advancements in factory-based agronomic management, artificial intelligence, and robotic technologies, the development of embodied intelligence—particularly supported by multimodal information perception and environmental interactive learning, and backed by China’s intelligent robotics industry would serve as a crucial technical pathway for enhancing the capability of agricultural robots in handling complex operational tasks.

Abstract:With the continuous growth in fruit and vegetable yields, the research and application of fully autonomous harvesting robots have become a major focus in the field of agricultural intelligence. However, the coordinated operation across the entire “perception execution planning” workflow of harvesting robots has still faced numerous technical challenges. To address this issue, existing research achievements were systematically reviewed, and the key technologies of fruit and vegetable harvesting robots were comprehensively analyzed, focusing on three core aspects: precise perception of fruit and vegetable targets, adaptive grasping control of harvesting targets, and autonomous harvesting behavior planning. First, starting from the perceptual foundation for autonomous operation, three categories of precise target perception method were reviewed, including deep learning-driven precise perception, multimodal information fusion perception, and active perception with viewpoint planning. These provided essential data support for subsequent execution and planning stages. Building upon this, the end-effector, as the core execution component, was examined in detail. The structural design characteristics of mechanically separated, vacuum suction, and flexible gripping end-effectors were categorized and summarized, and grasping control strategies adapted to different fruit and vegetable types were thoroughly discussed. Furthermore, the development status of robotic manipulators, ranging from industrial robotic arms to agriculture-specific robotic arms and multi-arm cooperative systems, was analyzed, and recent progress in autonomous harvesting behavior planning for robotic manipulators was systematically investigated. Finally, representative construction cases of fruit and vegetable harvesting robots were analyzed. The efficient synergy between perception systems, end-effector modules, and motion planning systems constituted the core imperative for the industrial deployment of fruit and vegetable harvesting robots. The robustness and stability of existing visual perception, grasping control, and motion planning algorithms were found to remain insufficient, while the degree of coordination among these modules demanded further improvement. Future research should concentrate on addressing these technical shortcomings and promoting multidisciplinary collaborative innovation to facilitate the comprehensive upgrading and widespread application of fruit and vegetable harvesting robots.

Abstract:The picking manipulators, as the core component of fresh fruit picking robots, are responsible for complex tasks such as approaching, grasping, and transferring fruits. The development of specialized picking arms is a critical research focus in this area. However, position errors of manipulators are increased by manufacturing tolerances, assembly errors, and joint flexibility. Existing research has concentrated predominantly on error calibration for manipulators with rotational joint configurations, while less attention has been devoted to the systematic and dynamic error calibration of specialized manipulators, such as long-stroke hybrid picking arm. An error calibration method for fruit picking manipulators was proposed by utilizing machine learning techniques to enhance motion accuracy. Firstly, an error model of the picking manipulator was formulated based on its geometric configuration, establishing the relationship between system parameters and terminal pose errors. Redundant parameters were subsequently eliminated via orthogonal triangle decomposition. To minimize the influence of system errors on motion accuracy, system parameters were identified by using the least squares method, while dynamic errors were predicted via a back propagation neural network. Finally, systematic and dynamic error compensations were implemented through an inverse kinematics error compensation framework. Simulation results indicated the average error identification accuracy of this method was 89.985%, and determinant coefficient R2 was 0.950. Physical experiment demonstrated that, after compensation, the average position error and root mean square error were reduced to 0.486 mm and 0.395 mm, respectively, while the average attitude error and root mean square error were reduced to 0.395° and 0.328°, respectively. The proposed method thus significantly enhanced the motion accuracy of picking manipulators.

Abstract:In dense orchard environments, apple-picking robots face significant challenges due to highly variable fruit postures, clustered fruit distribution, and severe occlusions from branches and leaves, all of which reduce the stability of suction-based acquisition and increase the risk of fruit damage. To enhance adaptability and operational reliability under such complex conditions, a pneumatic suction-twisting endeffector was developed. The device employed a compliant corrugated suction cup to achieve flexible negative-pressure adhesion and integrated a coaxial twisting-pulling mechanism that emulated the mechanical process of manual harvesting, thereby facilitating the fracture of the abscission zone and enabling low-damage fruit detachment. A quantitative model describing the relationship between blower flow rate and suction force was established based on the principles of negative-pressure adhesion. Furthermore, computational fluid dynamics was employed to analyze the effects of fruit posture, suction distance, and airflow rate on the suction flow field and surface pressure distribution. The simulation results indicated that variations in fruit posture influence suction performance primarily by altering the effective windward area and redistributing the pressure gradient over the fruit surface within the annular low-pressure field. As the suction distance increased from 1 mm to 5 mm, the surface pressure gradient decreased markedly, resulting in a substantial reduction in suction force. Increasing airflow rate strengthened the annular negative-pressure region and yielded a monotonic increase in suction force. Based on these analyses, the recommended blower operating range was determined to be approximately 15 ~20 kPa vacuum pressure with an airflow rate of around 240 m3/h. Field experiments conducted in dwarf high-density orchards demonstrated that the proposed end-effector achieved a maximum reliable suction distance of 4.41 mm and was capable of reliably grasping fruit under positioning errors of up to 5 mm. The optimal suction angle was determined to be 0°. The average suction-based acquisition time per apple was 1.97 s, with an overall capture success rate of 91% . Among different fruit sizes, medium sized apples (diameter 70 ~ 90 mm) exhibited the highest capture success rate, reaching 94.4% . No visible mechanical injury was observed on harvested fruit, confirming the feasibility and reliability of this design for soft and damage-free harvesting in complex orchard environments. The research result can provide theoretical support and technical reference for the structural optimization of pneumatic harvesting end-effectors and the development of intelligent robotic apple harvesting systems.

Abstract:Aiming to address the challenges of limited operating space, large variations in fruit-cluster height, and complex obstacle distributions in tomato harvesting under vertical cultivation conditions in protected greenhouses, a tomato harvesting robot that integrated visual perception, decoupled motion planning for the lifting mechanism, and a flexible gripping-cutting end-effector was designed and developed. The system adopted a hybrid configuration consisting of a 7-DOF collaborative manipulator and a rail-mounted lifting mechanism, and employed a dual-layer RGB D perception framework combining long-range and close-range sensing to enable accurate detection of ripe fruit clusters and precise localization of pedicel cutting poses. To improve the feasibility of harvesting trajectories under varying height and posture constraints, a decoupled planning method based on a reachability map for the lifting joint and manipulator was proposed. This method was further combined with improved PB PSO inverse kinematics optimization and attraction-field-guided RRT?path planning to achieve fast and collision-free generation and execution of harvesting trajectories. Field experiments in greenhouse environments demonstrated that the proposed method improved the accuracy of cutting-pose estimation and the success rate of grasping-pose planning by 5.9 percentage points and 7.5 percentage points, respectively, compared with baseline methods, achieving an overall harvesting success rate of 65.8%. These results validated the feasibility of the system in complex protected-greenhouse environments and underscored its potential to advance efficient and intelligent protected agriculture.

Abstract:Aiming to address the challenges of limited space, leaf occlusion, and fruit fragility in ridgecultivated strawberry harvesting, a four-wheel drive ( 4WD ) dual-arm picking robot system was developed. The system integrated a 4WD mobile platform, 6-DOF robotic arms, an RGB D camera, an air pump-driven flexible three-finger gripper, and a low-voltage brushless ducted blower, with the blower specifically designed to disperse occluding leaves and expose fruits. A three-stage framework (YOLO v8 seg PCA ICP) was proposed to enable accurate fruit instance segmentation and pose estimation. For trajectory planning, the rapidly-exploring random tree star ( RRT?) algorithm was integrated with Bspline interpolation to generate smooth, vibration-mitigated picking trajectories. Experimental results demonstrated that YOLO v8 seg achieved an mAP@ 0.5 of 92.7% , with 87.5% accuracy for occluded fruits. The overall accuracy of pose estimation reached 71.8% , with an average angular error of 9.7°±6.6°. After B-spline interpolation, the path generated by RRT?exhibited no abrupt variations and effectively mitigated joint vibrations. In greenhouse harvesting trials, the robot achieved an overall harvesting success rate of 86.5% , with 88.1% for non-occluded fruits and 71.4% for leaf-occluded fruits. The average time per fruit was 9. 6 s, and the undamaged picking rate for isolated fruits was 86. 8% . These results demonstrated that the proposed system delivered excellent operational performance in complex ridge cultivation environments, providing a feasible solution for the mechanized harvesting of ridge cultivated strawberries.

Abstract:In complex orchard environments, robotic fruit picking is challenged by branch occlusions, positioning inaccuracies, and low operational efficiency. A deep reinforcement learning-based framework for collision-free and efficient citrus harvesting with a robotic manipulator was proposed. The system integrated a YOLO 11 perception model for fruit classification and localization, together with a pressure prediction module for a flexible gripper to enable stable and damage-free grasping. An improved path planning algorithm, termed Attention LSTM HER SAC (ALH-SAC), was developed to achieve adaptive and reliable grasp control. The proposed algorithm extended the soft actor-critic framework by incorporating hindsight experience replay, an LSTM encoder for modeling temporal joint dependencies, and an attention mechanism to emphasize critical spatial features under occlusion. A customized reward function was designed to balance rapid target approach, obstacle avoidance, and optimal grasp orientation. In addition, a hybrid guidance strategy combining heuristic priors with a Cartesian-space linear interpolation prior was introduced to accelerate training convergence and enhance robustness. Simulation results demonstrated that ALH-SAC achieved a positioning accuracy of (10.35±4.18) mm, outperforming HER TD3 and HER DDPG baselines by over 12.5 mm on average. Orchard experiments further confirmed the effectiveness of the proposed method, achieving an average picking success rate of 84% and an average operation time of 10.58 s, validating its practical applicability in real-world citrus harvesting.

Abstract:Aiming to address the dual challenges of declining localization accuracy and harvesting difficulties caused by fruit branch occlusion in complex agricultural environments, a reinforcement learning-based occlusion suppression and path planning method for apple picking robots was proposed. By constructing a vision-motion collaborative active perception system, a dynamic occlusion suppression calculation model (OSCM) at the visual perception layer was developed based on YOLO v8 model. This model integrated color histogram-based region segmentation and boundary fitting estimation algorithms to generate real-time occlusion rates and suppression weights heatmaps for target fruits. At the motion decision layer, a depth camera was rigidly mounted on the robotic arm's end-effector, establishing a three-dimensional occlusion suppression matrix mapping mechanism. This mechanism discretized the depth space from the camera to the target into a three-dimensional grid matrix, where each grid cell contained the OSCM-derived occlusion rate and suppression priority. Building on this framework, a deep reinforcement learning-driven obstacle avoidance decision method was proposed. It employed a composite reward function incorporating occlusion rate penalties and harvesting timeliness, enabling the double deep Q-network (DDQN) to search for the optimal suppression path within the three-dimensional grid. Experimental results demonstrated that in high-occlusion scenarios with branch-leaf density no less than 60% , the system reduced the average occlusion rate by 33% , increased the harvesting success rate from 67% to 94.7% , and decreased the average picking time per fruit by 3.2 s. The research pioneered the closed-loop integration of occlusion suppression mechanisms with reinforcement learning decision-making, providing a novel methodology for enhancing agricultural robots'adaptability in dynamic environments.

Abstract:In agricultural plastic tunnel environments, the positioning accuracy of ultra-wideband (UWB) positioning systems is compromised due to variations in terrain elevation and ground surface irregularities, thereby affecting the navigation of picking robots and precise picking operations. A realtime correction and positioning method was proposed based on inertial measurement unit ( IMU) and UWB. Firstly, the grid map was corrected by using Rodrigues'rotation matrix and affine transformation. Subsequently, the UWB positioning data was preprocessed. By utilizing the rotation matrix obtained from the IMU that transformed the picking robot platform from its current attitude to the horizontal reference attitude, the lateral and longitudinal deviations from the UWB tag installation position to the actual position of the picking robot platform were calculated, thereby enabling real-time correction of UWB positioning errors caused by ground environmental conditions. Finally, autonomous navigation of the picking robot platform was achieved. The experimental results indicated that the maximum absolute error, maximum relative error, and root mean square error of the transformation-corrected grid map were 0.150 m, 2.27% , and 0.067 m, respectively. The real-time correction and positioning method based on IMU and UWB achieved lateral and longitudinal positioning deviations of less than or equal to 0.065 m and 0.069 m, respectively. Compared with the original UWB positioning data, the lateral positioning accuracy was improved by 26. 1% and the longitudinal positioning accuracy was improved by 13.8% . When the picking robot platform employing the aforementioned positioning method operated at autonomous navigation speeds of 0.15 m/ s, 0.30 m/ s, and 0.45 m/ s, the mean values of the lateral navigation deviation, longitudinal navigation deviation, and heading deviation were less than or equal to 0.057 m, 0.079 m, and 8.211°, respectively, with standard deviations less than or equal to 0.039 m, 0.069 m, and 4.307°, respectively.

Abstract:Aiming to address the degradation of robot mapping and localization accuracy caused by complex terrain and high similarity in unstructured environments, a SLAM algorithm based on multisensor loop detection, namely multi-sensor loop detection LiDAR-IMU LOAM ( MLD-LOAM) was proposed. Based on the LEGO LOAM algorithm architecture, this algorithm incorporated IMU data in the ground point cloud filtering phase after rapid filtering based on the distance distribution of laser points. This algorithm then performed a more accurate secondary filtering of the ground point cloud by calculating ground normals in real time. In the loop detection phase,the IMU and lidar data were fused by using a LiDAR confidence function designed with the sparsity of laser point cloud features. The fused odometry data triggered loop detection, which was then performed by an octree-modified normal distribution transform ( NDT), thereby improving system accuracy and efficiency. Experiments were conducted on the KITTI dataset and in unstructured outdoor real-world scenarios. Results showed that compared with LEGO-LOAM, MLD-LOAM improved localization accuracy by 11% and 30% in both the KITTI dataset and real-world scenarios. Compared with LIO-SAM and FAST-LIO, MLD-LOAM achieved better localization accuracy in real-world scenarios than FAST-LIO, but it was 2.8% less accurate than LIO-SAM. However, the speed of its memory consumption was only 50% of LIO-SAM and 32.4% of FAST LIO. The research result can provide a feasible solution for long-term mapping and localization tasks of outdoor robots that required low memory footprint.

Abstract:Accurate counting of tomato fruits is crucial for yield assessment and intelligent management. To achieve dynamic detection and counting of fruits among clustered branches in greenhouse environments, a dynamic tomato fruit counting method was proposed, primarily based on an improved YOLO v8n detection model and the ByteTrack tracking algorithm. A wavelet downsampling module and a P2 detection head were introduced into the YOLO v8n detection model, and an MLLA attention mechanism was designed to improve the model's detection performance in complex backgrounds. An adaptive low-score matching retry strategy was introduced based on ByteTrack. Finally, a counting method based on sequence region matching was proposed by using the improved ALRM Track. Experimental results showed that the improved YOLO v8n MAFP model achieved an average detection accuracy of 96.9% on the tomato fruit dataset, a 2.4 percentage points improvement over the original model. Combined with the improved ALRM Track algorithm of YOLO v8n MAFP, the multi-target tracking accuracy was increased to 88.2%, and the number of ID switching operations was reduced to 2. Using a sequence region matching mechanism for counting, the average absolute error in five greenhouse video experiments was only 1.4, with a counting accuracy of 96.6%, significantly outperforming traditional line-based counting and region-based counting methods. Greenhouse environment experiments demonstrated that the improved YOLO v8n ByteTrack model was suitable for the statistical needs of greenhouse tomato yield estimation.

Abstract:Stem and leaf obstruction, interlacing, and overlapping are common phenomena during the growth process of field-grown strawberries, posing significant challenges for fruit target detection by harvesting robots. To address target detection failures caused by foliage occlusion in strawberry cultivation, a synergistic algorithm integrating instance segmentation with occlusion detection, occlusion prediction, and airflow-optimized deoccultation was proposed. Firstly, a real-time segmentation model based on YOLO 11 seg was constructed to generate complete masks for strawberry fruits and occluding objects in complex scenes, followed by analysis to determine the current strawberry occlusion rate. For non-severely occluded regions (current occlusion rate no more than 70% ), the system directly targeted the geometric center of the strawberry mask for air-blowing intervention without initiating the spiral search-region growing algorithm. For heavily obstructed target areas (current obstruction rate greater than 70% ), a spiral search region growing model search algorithm was developed to locate the optimal airblowing intervention zone, precisely capturing the temporal characteristics of obstruction rate evolution. A lightweight CNN then used spiral features as input to accurately predict the post-air-blowing obstruction rate. Finally, a multi-parameter adjustable air-blowing device physically removed obstructions through integrated operation. Regarding occlusion rate prediction, the method achieved high accuracy in occlusion information forecasting (R2 was 0.925, RMSE was 2.57%), significantly enhancing estimation accuracy and adaptability to complex environments. Through the implementation of the integrated approach, i. e. , encompassing detection, prediction, and air-blowing removal, the method underwent field trials. Results demonstrated its effectiveness in reducing strawberry fruit occlusion rates under stemand-leaf shading scenarios, validating the algorithm's efficacy and providing a “ detection-predictionremoval” solution for crop de-occlusion in protected agriculture. Field trial results indicated that the system reduced the average occlusion rate from 68.5% to 12.8% across 90 severely occluded samples. In 82 samples (91.1% ), the occlusion rate fell below 15% , significantly enhancing the robustness of ground-grown strawberry identification and robotic harvesting adaptability in complex environments.

Abstract:Aiming to address the challenge of reliably acquiring 3D pose information of truss tomatoes for autonomous harvesting robots under conditions of severe occlusion and strong light interference in greenhouses, an improved 3D keypoint estimation model named TomatoPose3D was proposed. During the training phase, the model incorporated joint constraints between RGB images and 3D ground-truth keypoints to enhance structural consistency and generalization capability. In the inference phase, the model can end-to-end regress 3D keypoint coordinates from a single RGB image, thereby avoiding localization failures caused by sparse or missing point clouds. Based on the RTMPose3D baseline, the improved model introduced the global structure-aware MobileVit Block and the distribution-aware coordinate representation of keypoints (DARK) decoding strategy, improving localization accuracy while maintaining a lightweight architecture. Comparative experiments in greenhouse scenarios indicated that TomatoPose3D improved the PCK @ 0.05 score by 5.18 and 9.98 percentage points compared with RTMPose3D and SimpleBaseline3D, respectively. Without the assistance of depth information, the model achieved localization accuracy comparable to RGB D projection-based methods while demonstrating superior robustness. Furthermore, the model was deployed on an industrial-grade embedded platform accelerated by TensorRT, achieving an end-to-end inference speed of 37 f/s, which met the real-time spatial visual perception requirements of harvesting robots.

Abstract:Aiming at the problems of rising labor costs and an aging population, it is crucial to explore mango recognition technology and edge computing model deployment methods based on lightweight deep learning models to promote the development of lightweight and low-cost mango picking robots. A mango recognition method based on the improved YOLO 13n model was proposed by using YOLO 13n as the baseline model, and it was deployed on edge computing devices. Firstly, the SE module was introduced into the backbone network of YOLO 13n to improve the feature expression ability of the model. Secondly, a CBAM module was added to the neck network to fuse more feature information and strengthen the channel features of mango recognition, and highlight the mango region in the image. Then, the introduced SE module and CBAM module were used to reshape the YOLO 13n architecture to obtain the improved YOLO 13n model, and finally, the deployment of the improved YOLO 13n model on edge computing devices was realized by comparing the model deployment methods. The experimental results showed that the mango detection performance of the improved YOLO 13n model was P was 94.5%, R was 91.2%, AP0.5 was 95.6% and AP0.75 was 94.9%, which exceeded many lightweight models, including YOLO v8n, YOLO v9s, YOLO v10n, YOLO 11n, YOLO 12n and YOLO 13n. In addition, compared with the original YOLO 13n model, the P, R, AP0.5 and AP0.75 of the improved YOLO 13n model were improved by 0.6, 0.6, 1.1 and 1.5 percentage points, respectively, and the lightweight characteristics were maintained. To verify the effectiveness of the improved YOLO 13n model on edge computing devices, the improved model was deployed on the NVIDIA Jetson Orin Nano. The maximum inference speed for a single image reached 31.71 f/s. Compared with the original YOLO 13n model, the inference speed only decreased by about 0.4 f/s, still exceeding 30 f/s, meeting the real-time demand for mango recognition. By measuring the inference time on 50 randomly selected sample images, the average inference time was 36.88 ms, indicating the stability and reliability in real-time mango recognition. The research result can provide technical support for the development of lightweight, low-cost mango picking robots.

Abstract:Aiming to address the challenge of balancing detection accuracy and model lightweightness in automated passion fruit harvesting under densely occluded orchard environments, an improved YOLO 11n-based visual detection model was proposed. Partial convolution (PConv) was introduced in both the feature extraction stage and detection head to replace standard convolution blocks for lightweight optimization. A sliced 3D spatial and channel attention module ( SimAMs), integrating a feature map slicing mechanism, was designed to enhance cross-channel and spatial feature fusion. Experimental results showed that the improved model achieved a precision of 93.32% and mAP@0.5 of 93.08% , with increases of 1.28 percentage points and 0.26 percentage points over the original model. The parameter count, computation, and memory usage were reduced by 21.2% , 23.8% , and 20.0% , respectively. Detection speeds on GPU and CPU were approximately 1.36 times and 1.68 times faster than the average speed of the compared models, including EfficientNetV2 and FasterNet. Harvesting tests showed a single-fruit picking time of 13 s and a success rate of 91.7% at a depth range of 400 ~ 500 mm, 11.1 percentage points and 2.8 percentage points higher than that at near and far distances. The research result can provide effective technical support for high-precision, real-time detection and robotic harvesting of passion fruit in complex orchard environments with dense occlusion.

Abstract:Accurate quantification of young fruit dimensions and precise localization of thinning points on fruit stalks represented core challenges for intelligent apple fruit thinning. To address this, a 3D recognition and sizing method integrating RGB D data with geometric constraints was developed for young apples and fruit stalks during the thinning period. The YOLO v8 architecture was enhanced through integration of a convolutional attention fusion module (CAFM) and adoption of the SIoU loss function, yielding the proposed YOLO YF detection model. Mask R-CNN was subsequently employed for high-precision instance segmentation of young fruits and fruit stalks. RGB-D image acquisition was performed by using an Intel RealSense D435i depth camera, followed by rigorous image alignment and 3D coordinate transformation. A spatial subordination discrimination algorithm based on geometric features was established to determine fruit stalk relationships. Experimental validation demonstrated that the YOLO-YF model achieved 86.80% precision, 78.70% recall, and 84.40% mAP50 in clustered young fruit clusters detection. Mask R-CNN attained 91.20% segmentation precision and 80.94% IoU, enabling reliable differentiation between fruit stalks and petioles. In 3D measurements, root mean square errors ( RMSE) of 1.43mm (radial) and 1.28 mm (axial) were obtained for young fruits. The positioning error at the optimal thinning point (fruit stalk midpoint) was constrained to 1.20 mm. This approach can provide a technical pathway characterized by quantitative sizing and millimeter-level positioning accuracy for intelligent thinning equipment. The methodology demonstrated extensibility to clustered fruit management applications.

Abstract:Aiming to achieve accurate detection of citrus flowering stages in mountainous orchards, an improved citrus flower detection method was proposed based on YOLO v8m, named YOLO v8m-CFDNet. Within the YOLO v8m framework, a petal-aware convolution (PAC) module was introduced to optimize the C2f structure, thereby enhancing fine-grained feature extraction. The integration of MS CAM and SAM modules strengthened multi-scale and spatial attention representation, while the DySample dynamic up-sampling method alleviated edge blurring. In addition, an illumination-adaptive weighted cross-entropy loss was designed to improve robustness under backlight conditions, and Linear Soft NMS was adopted in post-processing to reduce missed detections of densely distributed targets. The model was trained and validated on Yongchun tangerine and Fuzhou mandarin datasets, with ablation, comparative, and generalization experiments conducted for comprehensive performance evaluation. The ablation results demonstrated that each module independently contributed to performance improvement, with the final model achieving 83.07% mAP@0.5, representing an 8.55 percentage points increase over the baseline. In comparative experiments, YOLO v8m CFDNet outperformed SSD, YOLO v5m, YOLO v6, YOLO v9e, and YOLO v10m, achieving a detection speed of 91.94 f/s with only 28.39 million parameters. Generalization experiments further showed a 6.64 percentage points increase in mAP@0.5 and a 7.2 percentage points improvement in recall under backlight conditions on the Fuzhou mandarin dataset. Confusion matrix analysis indicated the highest recognition accuracy (86.91%) during the full-bloom stage. Overall, the proposed YOLO v8m-CFDNet achieved a favorable balance among detection accuracy, real-time performance, and computational efficiency. It demonstrated strong robustness and generalization capability across citrus varieties and illumination conditions, providing an effective technical foundation for automated citrus flowering monitoring and intelligent orchard management.

Abstract:Aiming to reduce the impact of tractor jolts on the operating performance of the paddy field puddling and leveling machine, a passive vibration isolation system was added between them. The vibration data of the vibration isolation system were obtained through field experiments, and the effects of ground interaction on the system's performance were analyzed. To obtain the interaction relationship between the vibration isolation system and the ground, a vibration isolation system model for the interaction between the puddling and leveling machine and paddy field soil was established. To establish a complete model of the vibration isolation system, frequency domain integration of acceleration and the least squares method were used to identify the model parameters. In addition, the stiffness of the passive vibration isolation system was optimized based on the vibration isolation system model in contact with the soil. The results showed that due to ground interaction, the natural frequency of the vibration isolation system was increased by 8.5% , whereas the static deflection and the effective isolation frequency were reduced by 0.02 m and 6.4% , respectively. The identified parameters included an equivalent damping coefficient of 12 329 N·s/ m for the vibration isolation system, and equivalent stiffness and damping coefficients of 11 560 N/ m and 3 129 N·s/ m for the soil. The appropriate stiffness for the isolation system was determined to be 59 253 N/ m, which effectively isolated vibrations above 9 Hz. Under this condition, the RMS of the system’s displacement and acceleration responses were reduced by 29.1% and 58.4% , respectively.

Abstract:Aiming at the problems of poor soil-stone separation effect, high soil content rate, high stone leakage rate and poor machine adaptability of farmland stone picker under sandy soil conditions in Northwest China, a hydraulically driven chain-type farmland stone picker was designed, which can complete the picking, conveying and collecting of stones in the tillage layer at one time. Through the optimized design and theoretical analysis of key components such as the soil entry device and conveying separation device, the relevant structural parameters were determined. The motion analysis of stones in the conveying device was focused on, and the motion models of stone conveying in different stages were constructed. The collision of soil-stone mixture in the reflux process was used to increase the fragmentation degree of soil attached to the stone surface and soil blocks, so as to improve the separation efficiency. Through DEM MBD co-simulation, the simulation model of soil and stone conveying was established. Taking the operating speed, the spindle speed of the conveyor chain and the separation stroke of the conveyor chain as the test factors, and the stone leakage rate and soil content rate as the test indexes, the single-factor simulation test was carried out to determine the optimal interval range of each factor. Taking this interval as the factor range, the quadratic orthogonal simulation test was further carried out to obtain the optimal working parameter combination. The results showed that when the machine operating speed was 0.7 m/s, the spindle speed of the conveyor chain was 110 r/min, and the separation stroke of the conveyor chain was 2 700 mm, the performance was optimal, the stone leakage rate was 8.22%, and the soil content rate was 2.58%. Under the optimal parameter combination, the field verification test was carried out, and the stone leakage rate was 8.03% and the soil content rate was 2.62%, which met the national standards and industry requirements.

Abstract:Aiming at the problems of insufficient seed-filling time, poor seed-filling effect and unsatisfactory sowing uniformity of pneumatic precision seed meters during high-speed operation, a pneumatic high-speed precision seed-metering device for maize with double seed metering disks and a staggered hole layout was designed. The basic structure and working process of the seed-metering device were elaborated, a theoretical analysis was conducted on the force acting on seeds during the seed-filling process and the air chamber pressure, and the key structural parameters of the seed-metering disks were determined. Through DEM-CFD coupled simulation, the working performance of the seed-metering device under different vacuum levels was explored, and the influence of seed-disturbing bosses on the seed-filling performance parameters of the seed-metering disks was compared and analyzed. The results showed that the designed seed-disturbing bosses could effectively improve the seed-filling performance. Taking the qualified index, miss-seeding index and multiple-seeding index as evaluation indicators, single-factor and two-factor combination experiments of operating speed and air chamber pressure were carried out. The results indicated that at an operating speed of 12.14 km/h and an air chamber pressure of - 5.15 kPa, the seed metering qualified index reached 95.8%, while both the multiple-seeding index and miss-seeding index were 2.1%, which met the requirements of high-speed precision maize sowing. The research can provide ideas for the development of new high-speed precision maize sowing equipment.

Abstract:Traditional external slot-type fertilizer distributors exhibit significant pulsation during the fertilizing process, leading to poor uniformity of fertilizer application. To address these issues, a PID control method was proposed based on combination of segmented particle swarm optimization (PSO) and genetic algorithm (GA), and a corresponding precision fertilization control system was designed. This method leveraged the ability of PSO to quickly find local optimal solutions and the efficient global search capability of GA to achieve rapid response and high-precision fertilizer flow regulation in the precision fertilization system. The performance of the control algorithm was evaluated through fitness tests and segmented optimization tests, and a fertilization flow test platform was established for bench tests and soil bin tests to verify the adaptability of the controller in the field. The results showed that the GA PID algorithm optimized by PSO demonstrated significant advantages in the fitness test, converging to 0 in only 13 iterations, with higher precision and faster iteration speed compared with GA and PSO algorithms alone. The segmented optimization test indicated that the shortest response time of the precision fertilization control system was 0.36 s, which was 91.44% reduction compared with the system without the PSO-optimized GA algorithm. The average fertilization accuracy in bench tests and soil bin tests was 98.07% and 97.69%, respectively. These results demonstrated that the control algorithm met the requirements of rapid response and high-precision fertilization, enhanced the robustness of the control system, and provided theoretical and practical support for high-precision regulation of solid granular fertilizers.

Abstract:High-density transplanting with small plant spacing, small hole size, and large planting depth has strict requirements for hybrid kinematic targets such as the swing angle of the key operation section of the duckbill, the position deviation between the movement point of the mechanism end and the target point, and the trajectory height. Aiming at the poor performance of existing seedling planting mechanisms, this paper carries out the research on the optimization design of single-row two-stage transmission planetary gear train mechanisms with hybrid kinematic design requirements. Taking parameters such as the rotation angle of the first planet carrier, the rotation angle of the middle wheel, the length of the first planet carrier and the end-effector components as optimization variables, and taking the minimum position deviation between the movement point of the mechanism end and the target point and the maximum convexity value of the non-circular gear as optimization targets, the posture angle of the key point, the swing angle of the operation section and the position requirement under the absolute coordinate system are transformed into additional optimization objective functions according to the operation requirements. The multi-objective genetic algorithm is used to solve the problem, and the parameter group of the multi-objective optimization design mathematical model of the mechanism under hybrid kinematic conditions is obtained. According to the seedling planting requirements with the maximum planting depth of 80 mm and the plant spacing of 90 mm, the first planetary carrier and the planting trajectory are obtained through optimization calculation. A prototype was developed and tested on a bench and in a soil bin. The bench test results showed that the average seedling planting success rate was 93.67% and the missed planting rate was 2.33%. The soil bin test results showed that the average seedling planting success rate was 91.42% and the missed planting rate was 3.45%, which met the requirements of high-density transplanting operation.

Abstract:The longitudinal intermittent precise delivery of soft seedling trays is a prerequisite for precise transplanting of rice seedlings by a seedling transplanter. Aiming at the problems of rigid impact and unsatisfactory self-locking effect in the existing cam-type intermittent feeding device, a gear-linkage-shaped-slot-wheel combination longitudinal precise feeding device with a flexible impact characteristic was proposed. The kinematic model of the shaped-slot wheel mechanism was established, and the characteristics of angular velocity and angular acceleration of the slot wheel during movement were analyzed. Based on the minimum rewinding radius of the seed tray, the number of slots, movement and stop times of the slot wheel were determined. A pair of double circular pins was introduced to solve the overshooting problem of the slot wheel caused by inertia during rapid rotation. An end face braking mechanism was used to eliminate the accumulated error in the rotational angle of the driving gear due to its inertia, so as to achieve precise intermittent motion of the feeding device. The spring preload force was determined through calculation. Finally, a two-row seed tray box suitable for seedling transplanting was developed, a test bench was manufactured, the angular deviation of the device was measured, and a longitudinal seed harvesting test was conducted. The test results showed that the single longitudinal feeding angular deviation of the device was within the range of -1.5° to 1.8°, and no error accumulation occurred during seed harvesting. By matching the seed harvesting test bench with the transplanting mechanism, accurate seed supply and clamping and pulling of seedlings can be achieved, i. e. , the longitudinal seed harvesting device design met the operational requirements.

Abstract:During the greenhouse seedling tray cultivation process, phenomena such as missing seedlings and poor seedling development occur, necessitating the elimination and replanting of substrate in cavities with missing seeds or stunted growth to enhance yield. To address the challenge of eliminating low-quality seedlings in greenhouse seedling trays, a negative pressure elimination device was designed based on the physical parameters of the seedling tray substrate. Considering the tendency of low-quality seedling substrate to cause pipeline blockages during negative pressure elimination, a swirling flow anti-clogging method was implemented by incorporating a threaded structure at the end tube. Pre-experiments identified key factors affecting elimination success rate and pipeline blockage rate in the end tube structure: the cross-sectional shape of internal threads and the parameter range of thread turns. A Fluent EDEM coupled simulation method was employed to investigate the impact of these factors on substrate fragmentation. Response surface analysis was used for experimental validation, with a three-factor, three-level test design focusing on thread cross-sectional shape, number of thread turns, and end tube lifting speed. Optimization targeting elimination success rate and blockage rate revealed the optimal parameter combination: triangular internal thread cross-section, eight thread turns, and a lifting speed of 0.13 m/s. Finally, using the optimized parameters mentioned above, a defective seedling adsorption test was conducted. The results showed a seedling removal success rate of 99.4% , a blockage rate of 0.01% , meeting the requirements of the seedling tray for subsequent replanting operations.

Abstract:Aiming to address the problem of severe kernel damage caused by high-frequency, intense impacts from threshing elements during corn grain direct harvesting using conventional rotary drum threshing devices, a low-damage threshing strategy was proposed. The strategy involved first pre-fragmenting intact ears into segments to loosen the kernels, then placing these ear segments between upper and lower threshing boards. Threshing was achieved through reciprocating flexible rubbing, avoiding rigid impact on the kernels. Based on this approach, a low-damage vibrational threshing device for fragmented corn ears was designed where ear fragment were processed between reciprocating boards equipped with flexible polyurethane and reinforced nylon elements to minimize impact stress. Through theoretical analysis, the main structural and operational parameters of the device were determined, and a vibration dynamic model for the interaction between the threshing device and corn fragments was established. Using the exit board spacing, upper board frequency, and lower board frequency as experimental factors, and threshing efficiency and damage rate as evaluation indicators, a three-factor, three-level Box Behnken experimental design was implemented. Variance analysis of the results was performed, and regression models were used for parameter optimization. The optimal parameters were determined as follows: exit board spacing of 13.6 mm, upper board frequency of 12.9 Hz, and lower board frequency of 37.6 Hz. Verification tests under this parameter combination yielded an average threshing efficiency of 97.66% and an average damage rate of 1.96%. These results were largely consistent with the regression model optimization results and met the requirements for low-damage corn threshing.

Abstract:In response to the low qualified rate of root-cutting in Chinese cabbage operations in Hebei Province, which was caused by complex terrain and mismatched cutting parameters, a cabbage rootcutting device mounted on a crawler gantry chassis was designed. By adjusting the extension of the electric push rod in the cutting mechanism and the rotational speed of the cutting motor, the device realized adjustable cutting height, angle, and speed, thereby improving cutting efficiency. The system mainly consisted of a profiling mechanism, a cutting mechanism, a crawler walking mechanism, and a control system. To optimize operational performance, static and dynamic analyses of the cutting mechanism were conducted, and the adjustable range of cutting height was determined as 90 mm, the angle range as 0°~42°, and the rotational speed range as 0~600r/min. Furthermore, a finite element simulation model of the cabbage stem cutting process was established by using ANSYS/LS-DYNA. Taking the stem reaction force as the evaluation index, cutter angle, cutter speed, and forward speed were selected as influencing factors for simulation-based optimization tests. The simulation results showed that when the cutter angle was 10.487°, the cutter speed was 271.603r/min, and the forward speed was 0.204m/s, the minimum stem reaction force was 152.068 N. After rounding the optimized simulation parameters to align with practical conditions, field validation tests were carried out. The results showed that when the cutter angle was 10°, the cutter speed was 275r/min, and the forward speed was 0.2m/s, the average qualified root-cutting rate reached 93.23% , with an average damage rate of 4.05% . Under these conditions, the key components operated stably, root-cutting consistency was high, and the damage rate remained low. These findings provided a valuable reference for the design of low-damage harvesting equipment for Chinese cabbage and for the configuration of its operating parameters.

Abstract:Microwave detection technology is applicable in the field of winter bamboo shoots detection because of its high efficiency, non-invasiveness, low cost and other characteristics. In order to clarify the optimal center frequency of microwave signals in the process of winter bamboo shoots detection, an electromagnetism coupling model of winter bamboo shoots-soil was constructed, and the change rule of microwave detection frequency was researched. Firstly, the E5080B ENA vector network analyzer was used to test the dielectric properties of winter bamboo shoots and soil, and analyze the influence of signal frequency on the dielectric properties of winter bamboo shoots and soil with different water content. At the same time, ANSYS HFSS 15.0 simulation software was used to design a back-cavity dish antenna with an operating frequency in the range of 1.5 ~ 2.0 GHz. The winter bamboo shoots-soil electromagnetism coupling model was established based on the data of dielectric constants corresponding to soils with different water contents in this frequency range. Based on this model, simulation and comparison tests were carried out for winter bamboo shoots with a water content of 86.82% and soil with water contents of 17%, 20%, 23%, 26%, and 29%, respectively, to investigate the response characteristics of the S?? parameter with the variation of dielectric. The results showed that as the soil water content became larger, the S?? parameter became smaller and the signal loss became larger, the winter bamboo shoots became more capable of absorbing the energy signals transmitted by the antenna, and it can distinguish the return signals with and without the winter bamboo shoots more efficiently;at the same time, since the antenna had the highest transmission loss at the frequency of 1.6 GHz, the optimal center frequency was 1.6 GHz.

Abstract:Aiming to address the issues of low weighing accuracy and suboptimal efficiency caused by multiple disturbances during the weighing process of tea packaging machines, focusing on weighing signal denoising and controling algorithm optimization, a comprehensive method integrating an optimizing Kalman filter algorithm with a three-stage fuzzy PD and adaptive iterative learning control strategy was proposed. Regarding signal denoising, addressing the insufficient noise reduction effectiveness of traditional Kalman filtering across weighing stages, a phased optimization strategy was proposed: during the dynamic feeding stage, exponential preprocessing was fused with Kalman filtering to suppress highfrequency noise;during static weighing, process disturbance noise covariance was reduced to enhance filtering stability, and final weight values were obtained through weighted averaging based on the convergence degree of the state covariance matrix. In the hopper opening phase, tracking lag was eliminated by adjusting the Kalman gain to its optimum value. During hopper closing, weighted limiting was introduced to mitigate spike disturbances. Regarding the control algorithm, a three-stage fuzzy PD control strategy was designed, dividing the dynamic feeding process into coarse feeding, deceleration feeding, and fine feeding stages. Combining fuzzy theory, PD parameters were tuned online to balance the deceleration stage through dynamic parameter adjustment, facilitating a smooth transition. Furthermore, to address overshoot after the vibrating feeder stops, an adaptive iterative learning algorithm was introduced during the fine feeding stage. By iteratively adjusting the advance stop amount of the vibrating feeder, the actual weighing value converged more rapidly toward the target value. Test results showed the actual weighing deviation for Biluochun and Longjing green teas was controlled within ± 0. 06 g relative to the target mass, while Wuyi rock tea remained within ±0.12g. Moreover, all three tea types completed weighing operations within a short timeframe, with weighing time variations for identical target weights controlled within ±1s. In summary, this method effectively enhanced the precision, efficiency, and stability of the automatic tea weighing system, providing a viable solution for optimizing the performance of automatic tea packaging machines.

Abstract:Taking the optimization of the active guide vane airfoil shape of the pump turbine as the objective, the active guide vane shape was parameterized by B-splines, and in order to ensure the structural strength of the active guide vane airfoil, the pivot diameter of the guide vane and the shape of the guide vane along the height direction of the vane were kept unchanged in the optimization of the active guide vane of the pump turbine, a total of 12 control points of the active guide vane airfoil shape up and down, and the control points were changed by the use of Latin hypercube sampling to realize the geometric reconfiguration of the active guide vane airfoil shape in the given interval. The geometrical reconstruction of the guide vane airfoil was achieved by changing the control points in a given interval by using Latin hypercube sampling, and the influence of 360 different guide vane airfoils on the efficiency of the pump-turbine was analyzed with the efficiency as the objective function, and the comprehensive loss coefficients were introduced by taking into account the flow mechanisms of the pump and the turbine in two working conditions. Numerical simulation was used to validate the prediction data of the RSM HDMR surrogate model for the active guide vane airfoil. The results showed that the optimized guide vane profile shifted the high-efficiency point backward without changing the energy characteristics of the internal flow at the original pump operating point, and at the same time, it improved the internal flow characteristics of the turbine operating condition, reduced the loss of turbulence kinetic energy, and reduced the energy dissipation at the tail, and improved the efficiency by 0.379 percentage points, which proved that the optimization method of this guide vane airfoil profile was feasible, and it can provide methodological and theoretical guidance for the design and modification of the pump turbine's movable guide vane airfoil profile.

Abstract:There are a large number of vortices in hydraulic machinery, and studying the influence of vortex structure on blade force is the key to revealing the internal flow mechanism and improving design level. However, most analysis methods are difficult to quantify the influence of vortices. The force decomposition method (FDM), as a post-processing technique, can express the pressure force acting on the boundary immersed in the fluid as the sum of the forces generated by different effects, including vortices, in the flow field. Therefore, taking a centrifugal pump as the research object, the forces on the blades caused by different effects in the flow field were obtained by using FDM method. The results showed that FDM can accurately analyze the forces of different effects in the flow field, and the deviation can be controlled below 15%. Under different operating conditions, the force generated by vortices dominated, while the force generated by viscosity can be ignored. The force generated by the vortex was mainly caused by the flow impact at the blade inlet and the corresponding flow separation, as well as the wake at the blade outlet. Under low flow rate conditions, the vortex force generated by inlet impact and flow separation had the highest proportion. The research result can provide an approach for quantifying the impact of vortices on hydraulic machinery and reveal the relationship between the forces generated by vortices and local flow phenomena.

Abstract:The inflorescence developmental stage of alfalfa serves as a critical physiological indicator determining its nutritional value and yield, making real-time and precise monitoring highly significant for forage quality regulation and cultivation management optimization. To address the technical challenges in large-scale alfalfa cultivation, including small inflorescence targets (8 ~ 32 pixels), dense distribution, complex backgrounds, and high demands for real-time monitoring, an intelligent monitoring method for alfalfa inflorescences was proposed by integrating low-altitude UAV image and lightweight deep learning. Firstly, a multi-scenario, multi-variety, and multi-temporal low-altitude UAV RGB dataset of alfalfa inflorescences was constructed, enhancing model generalizability through sample filtering, hybrid sample selection, and data augmentation strategies. Secondly, an improved YOLO 11n ICA model was designed. To effectively enhance the model's ability to detect dense small targets in complex backgrounds, the C3K2_INXB module was innovatively designed and the max-avg context anchor attention (MACAA) module was constructed. Finally, a real-time alfalfa inflorescence monitoring system was implemented based on a Web-cloud collaborative architecture, achieving an inference speed of 38 f/s. Experimental results demonstrated that the proposed method achieved a mean average precision (mAP@50) of 97.5% and a small-target recall rate of 92.9% on the self-built low-altitude alfalfa inflorescence dataset. Field validation tests showed that the system achieved a recognition accuracy of 95.28% and the miss-detection rate was 4.72%. Additionally, it automatically generated spatiotemporal distribution heatmaps of alfalfa inflorescences, providing decision-making support for precision agriculture management. The research result can provide a high-precision, lightweight, and real-time approach for rapid inflorescences counting in complex field environments, offering significant practical value for advancing intelligent pasture management.

Abstract:In the scenario of large-scale automatic apple warehousing, the counting model based on computer vision must balance lightweight design with detection accuracy. Traditional models involve large parameters and high computational costs, making real-time operation challenging;moreover, in complex environments characterized by dense apples and severe occlusion, issues such as boundary blurriness and high false detection rates arise. To address these challenges, an improved CGW-YOLO v8 model was proposed. Firstly, by replacing the C2f module in the backbone network with the lightweight GhostNet module and incorporating a feature channel reweighting mechanism, the model's parameter count was significantly reduced. Secondly, the CSPHet module was employed, which utilized heterogeneous multibranch convolution and a dual-path feature fusion strategy to enhance the boundary distinguishing capability of densely packed apples while decreasing the number of parameters. Lastly, a loss function based on Wasserstein distance loss was adopted to replace the traditional IoU metric, effectively reducing the false detection rate in densely stacked scenarios. Experimental results indicated that the model's mean average precision (mAP@0.5) was improved to 95.8%, representing a 1 percentage points increase compared with that of the original model, with precision and recall increased by 1.1 percentage points and 1.3 percentage points, respectively, compared with that of the original model. Both parameter count and computational volume were decreased by 24.4% and 23.2% relative to that of the baseline model. To meet the dual requirements of real-time counting and accuracy in the inventory production process, the DeepSORT tracking algorithm was integrated to achieve continuous tracking and accurate counting of apples across video frames. A counting strategy based on trajectory management was designed, which counted only when the target first crossed a virtual counting line, effectively avoiding issues of duplicate statistics and missed counts. Experimental results demonstrated that the proposed improved method exhibited strong robustness and high counting accuracy, particularly in complex backgrounds, such as densely arranged apples and partially obscured scenes.

Abstract:Cotton pigment glands are rich in gossypol, which holds significant value in agricultural pest control, medical pharmacology research, and other fields. Accurately obtaining information on the area and quantity of pigment glands is the key to evaluating gossypol content. However, pigment glands are small in size, numerous in number, and densely distributed. They account for a very low proportion in the entire leaf image and are easily interfered by leaf veins and background noise. Therefore, the rapid and accurate identification of pigment glands in cotton leaves remains challenging. To address the above problems, a portable field device for acquiring cotton leaf images was developed, which can obtain high-quality images with a simple background without damaging the cotton leaves. Meanwhile, a lightweight semantic segmentation network named Dual-GlandNet was proposed. This model only conducted partial cross-resolution feature interaction for high and low resolution branches. Specifically, the CBAM attention module was introduced into the high resolution branch to enhance the expression ability of fine-grained features. For the low-resolution branch, the CoordAtt coordinate attention module and the three-way separable dilated convolution Lite SepPP were added to strengthen the capability of global semantic feature extraction. Experimental results showed that the proposed Dual-GlandNet model achieved a mean intersection over union (mIoU) of 80.6%, the F1-score of 86.5%, an inference time of approximately 17 ms per image, and a parameter count of only 6.79×10^6. Compared with other mainstream semantic segmentation models, this model achieved a better balance between accuracy and speed, providing a deployable technical solution for real-time and non-destructive detection of pigment glands in cotton leaves. It was of great significance for assessing gossypol content in cotton leaves, breeding high-quality varieties, and implementing precise field management.

Abstract:Effective pest monitoring is crucial for high-quality vegetable cultivation. While deep learning-based pest detection methods excelling at detecting large- and medium-sized pests, they face challenges with small-sized pests. To address the problem, a you only look once (YOLO)-based small-sized vegetable pest detection method was presented, named YOLO SVP. To emphasize crucial small-sized pest features and improve feature fusion, a dynamic weighting attention (DWA) mechanism was constructed and integrated into the C3k2 block of YOLO 11, yielding a new block denoted C3k2 DWA. Additionally, to preserve critical spatial information during downsampling and reduce the loss of small pest features, a space-to-depth downsampling (SPD Down) block was proposed. Besides, to alleviate the severe weakness of bounding box regression in the case of small pests, the normalized Wasserstein distance (NWD) loss function was introduced. Experimental simulation on a self-built vegetable pest dataset demonstrated the effectiveness of the proposed YOLO SVP, which achieved 85.7% F1 score, 89.3% mAP??, and 54.9% mAP??:??, outperforming YOLO 11 by 4.5, 3.8, and 4.3 percentages points, respectively. For the Frankliniella occidentalis (small-sized pest), the detection performance improved the F1 score, mAP??, and mAP??:?? by 6.3, 8.5, and 5.0 percentages points, respectively. This research provided a paradigm for adapting deep learning architectures to challenge small-sized object detection tasks in precision agriculture, which would provide important support for the effective monitoring of vegetable pests.

Abstract:Aiming at the problems of insufficient feature extraction and easy tracking loss of feature point-based SLAM systems in texture-less scenarios, as well as improving the initialization accuracy and robustness of the system in challenging scenarios, the line features on the basis of the ORB SLAM3 framework were incorporated and the visual inertia initialization was improved. Firstly, the LSD algorithm and LBD descriptor were incorporated into the front-end visual odometry part for line feature extraction and matching, the point and line feature reprojection error models were established, and the BA method based on nonlinear optimization was used to minimize the reprojection error, while the adaptive factor was introduced to dynamically adjust the weights of the line features. Then the gyroscope bias estimator was constructed by extending the binocular MNEC constraints, adopting the rotation-translation decoupling optimization strategy and introducing the residual evaluation mechanism to ensure the reliability of visual inertia initialization, while the IMU residuals, the feature point reprojection errors, and the line reprojection errors were jointly used as the constraints of nonlinear optimization for the estimation of the camera position. Experiments were conducted in the euroc dataset and real scenes, and the results showed that compared with the pre-improved ORB SLAM3 algorithm, the improved algorithm improved the localization accuracy by 22.9% under the dataset and reduced the offsets by 1.4 m in the real environment, thus verifying the feasibility and effectiveness of the improved algorithm.

Abstract:The development of large language models (LLMs) has significantly propelled the latest advancements in natural language processing (NLP). These models have been built upon complex deep learning architectures, typically Transformer, characterized by billions of parameters and extensive training data, enabling them to achieve high precision across a variety of tasks. However, the absence of agricultural machinery-specific textual data for training in existing general large models has severely limited their performance in the research, development, manufacturing, and application of agricultural machinery. To address this issue, the specific needs of agricultural machinery for large models were analyzed and a Chinese-compatible agricultural machinery large model named "LeiSi" was proposed, catering to various groups such as university faculty, students, designers, and users. The overall architectural design of the LeiSi LLM was outlined, which included three parts: LeiSi-torch, LeiSi-ingenuity, and LeiSi-plough, aiming to provide target groups with diversified and customized services such as agricultural machinery professional knowledge Q&A, agricultural machinery design and manufacturing advice, and agricultural machinery field operation control. Subsequently, the LeiSi-torch large language model was taken as an example to elucidate the construction of a Chinese agricultural machinery dataset and the methods for model fine-tuning and automatic evaluation. Utilizing LLaMA 3.1-8B-Instruct, Mistral-7B-Instruct-v0.3, and Qwen 2.5-7B-Instruct as base models, supervised fine-tuning on each was conducted and the performance of the fine-tuned models was evaluated by using ROUGE and BLEU as evaluation metrics. Finally, manual evaluation was employed to assess the Q&A results of LLaMA 3.1, GPT-4o, Mistral, Qwen 2.5, and the LeiSi-torch model. The results from both automatic and manual evaluations indicated that the LeiSi-torch model demonstrated superior performance in terms of accuracy, professionalism, and usability. The research outcomes can provide insights and references for the development of intelligent agricultural machinery and smart agriculture.

Abstract:Aiming at the problems of overlapping triples, the difficulty in extracting nested entities and complex entities in the text data within the field of citrus diseases and pests, a joint extraction method for citrus diseases and pests entity relationships based on dual-pointer network annotation-cascade binary tagging framework for relational triple extraction (DPNA-CASREL) was proposed. By combining the pre-training model robustly optimized BERT pre-training approach with whole word masking and extended training data (RoBERTa-wwm-ext) with the bi-directional long short-term memory (BiLSTM) to construct an encoder, multi-dimensional vector encodings of the text were obtained. According to the semantic characteristics of citrus diseases and pests, a decoding network with dual-pointer network annotation was designed. The multi-level-pointer-network annotation method was introduced in decoding the head entity, and a complex entity labeling strategy was adopted in the decoding network of the tail entity to enhance the model’s extraction performance for complex entities. By adopting a complex entity labeling strategy in the tail entity decoding network, the synchronous extraction of entity relationship triples was realized, and the problems of overlapping triples and nested entities were solved. Experimental results on a self-built citrus diseases and pests dataset showed that the precision, recall, and F1-score of the DPNA CASREL model reached 82.12%, 81.97%, and 82.05%, respectively, which was superior to those of other models. Compared with CASREL, the F1-score of the nested and complex entity extraction were improved by 8.16 percentage points and 6.58 percentage points, respectively. This method can effectively solve the problems of entity nesting and unclear entity boundaries. It can provide a basis for citrus diseases and pests knowledge-graph construction and other downstream tasks.

Abstract:Aiming to address the prevalent issues of high daytime temperature and high nighttime humidity in solar greenhouse cherry production, a mixed-ventilation strategy was developed and validated based on forced convection to optimize the indoor thermal-humidity environment and enhance fruit quality. Taking typical solar greenhouses in Yulin (planted with cultivars ‘Brooks’, ‘Summit’, and ‘Rainier’) as the research object, computational fluid dynamics (CFD) simulations and environmental monitoring were firstly employed to compare the effects of natural ventilation and different forced convection modes (rear-wall fans blowing downwards/upwards) on the temperature, humidity, and airflow fields, thereby determining the optimal ventilation scheme. Subsequently, field comparison experiments were conducted to verify the practical effectiveness of the optimized strategy on environmental regulation and its impact on fruit quality during the fruiting stage. Both CFD simulations and field measurements indicated that the mixed-ventilation strategy with “rear-wall fans blowing downwards” performed optimally. Compared with natural ventilation, this strategy effectively reduced the daytime canopy temperature (max. reduction of 3.9℃) and significantly suppressed high nighttime humidity (maximum reduction of 14.6 percentage points in the plant zone) while avoiding excessive cooling. Quality assessments revealed that this strategy significantly improved the commercial and nutritional value of the fruit: for ‘Brooks’, single fruit weight was increased by 19.8% and firmness by 31.6%; for 'Summit', the a? value (redness) and total phenolic content were increased by 26.9% and 36.1%, respectively; the edible rate and total soluble solids of ‘Rainier’ were also significantly enhanced. The sugar-acid ratio was significantly improved across all cultivars. The proposed mixed-ventilation strategy effectively overcame the environmental bottlenecks in solar greenhouse cherry production. It represented an efficient, low-cost environmental control technology for achieving high yield and quality, providing an engineering basis and practical solution for precision climate control in protected horticulture.

Abstract:In intensive sheep farming, the lack and backwardness of environmental management technologies are key factors contributing to the deterioration of sheep house environments. Accurately predicting changes in sheep house environmental parameters are crucial for ensuring the healthy growth of sheep and improving the economic benefits of the sheep farming industry. To accurately understand the PM?.? concentration patterns within sheep houses, the wavelet transform (WT) was used to decompose and reconstruct sheep house environmental parameter data to eliminate data noise. The sparrow search algorithm (SSA) was then used to optimize the number of hidden layer neurons, learning rate, and batch size of the LSTM model. This approach also adjusted the input model parameters to avoid randomness in parameter selection and further improve model performance. Experimental results showed that the WT-SSA-LSTM model outperformed other prediction models in all metrics, with MAE, RMSE, MSE, NRMSE, and R2 reaching 0.3497 μg/m3, 0.6004 μg/m3, 0.3605 μg2/m?, 0.0057, and 0.9981, respectively. This demonstrated the high accuracy and stability of the proposed WT-SSA-LSTM prediction model, effectively providing guidance for monitoring and regulating PM?.? levels in intensive sheep farming facilities.