Chassis assembly is one of the core components in mobile agricultural power machinery. A universal chassis can carry the functional tools to realize the various activities in agricultural production. Among them, the oil-operated track chassis are widely used in recent years, due to their low cost, mature technology, sufficient power, high range, and easy maintenance. But their low handling accuracy and severe vibration have posed great challenges to the application of automatic navigation. In this study, the concept of clutch-braked track chassis was proposed as a low-cost agricultural oil-operated track chassis platform. Stronger traction was achieved during steering, compared with differential track chassis vehicles. The RTK-GNSS-based automatic navigation system was built with the STM32F303 as the main controller. The working path of agricultural machines generally consisted of a straight line in the actual operation. The process of tracking was divided into the desired path of the track chassis platform online and the straight line tracking, according to the online point (the online point referred to the sampling point with a lateral deviation less than 3.0 cm, heading deviation less than 2.0°, and the closest distance to the starting position). The kinematics model was established using the transmission and motion of the platform vehicle. A virtual search light pathfinding tracking (VSPT) was proposed to represent the pseudo-code of the VSPT. The fuzzy pre-scanning control was enhanced to search for the target points within the searchlight's field of view in real time. The steering of the track vehicle was controlled to expose the target points within the field of view at all times. In addition, the field of view angle was dynamically adjusted for the tracking oscillation caused by the lateral deviation and speed change. The lateral deviation index and a virtual target point judgment were utilized with a positive proportional relationship of speed. The logical process was designed for the data flow diagram. Direct memory access (DMA) data transfer was used with the idle interrupt and cyclic mode to complete the reception of variable-length byte data. The operation efficiency of the single-chip computer was improved to design the logic flow for the data flow diagram. The improved model was verified to calibrate the relevant parameters by simulation tests. The better navigation was achieved at the chassis forward speed of 0.4 m/s, where the parameters lateral deviation index λ, field-of-view gain k₁, and target gain k₂ were taken as 1/4, 0.005 rad∙m and 6.0 s⁻¹, respectively. Subsequently, the calibrated parameters after simulation were used for the field tests. The results showed that: the average online distance was 1.64 m, while the average lateral and heading deviations were 0.44 cm and 1.57°, respectively, for 6 different initial positions under concrete road conditions. The navigation tests showed that the increasing speed led to a decrease in the navigation accuracy. The appropriate correction of parameters was conducted to maintain better navigation. The average lateral and heading deviations were 0.75 cm and 1.05°, respectively, under 3 speeds, where the average number of corrected deviations was 4.7 times. The number of corrected and heading deviations were reduced under the field dirt road conditions, due to the increasing adhesion coefficient of the dirt road and relatively smooth steering. But the navigation effect was similar under the same parameters in both road conditions. Therefore, the VSPT performed better tracking and adaptability for the control of the clutch-braked track platform. This finding can provide an efficient and stable control scheme for the clutch-braked crawler platforms with navigation algorithms

Pile picking has been widely used for harvesting green Sichuan pepper branches. In this targeted pruning, the fruit-bearing branches are selectively cut to preserve the short stumps of a specific length. Part of the branch structure can be maintained to support the subsequent growth in the overall health of the plant for harvesting efficiency. This study aims to accurately recognize the branches and then determine the optimal cutting points for the efficient short-stump cutting under the complex field environments with dense foliage and varying illumination. A Sichuan pepper harvesting robot was also selected as the research subject. A systematic investigation was then made to locate the short-stump cutting points on the main branches of the green Sichuan pepper using the U-Net deep learning network and RGB-D depth camera. Semantic segmentation was integrated with the branch identification with the depth information for the spatial localization. A complete processing pipeline was established from the image acquisition to the cutting point coordinates. Firstly, the traditional U-Net model was improved to replace its backbone network with ResNet50 that embedded with a Coordinate Attention (CA) mechanism. Spatially fine-grained features were captured to enhance both the boundary completeness and segmentation precision of the branch structures. The Squeeze-and-Excitation (SE) attention mechanism was added to the feature splicing stage of the U-Net model. Channel-wise feature responses were also recalibrated adaptively. Thereby, a robust segmentation model was constructed for the main branches and trunk of the Sichuan pepper. Target structures were effectively distinguished from the complex backgrounds, including the leaves, fruits, and interfering branches. Secondly, the segmented images of the main branches and trunk were binarized using the Zhang-Suen algorithm. The centerline of the main branches was extracted to integrate the depth information from the RGB-D camera with OpenCV image processing. The pixel length in the pixel coordinate was converted into the physical length in the physical coordinates using camera intrinsic parameters, including the focal length and pixel size. The actual length in the world coordinate was then transformed to incorporate the depth measurements from the RGB-D camera. Spatial geometric transformations were applied to the accurate coordinate mappings. The length mapping between the world and the pixel coordinate system was achieved through the accurate metric-scale measurements of the branch dimensions in three-dimensional space. The 40 mm stump length was determined. The predefined short stake length of 40 mm was then accurately mapped from the world coordinate system to the pixel scale in the RGB images. Finally, the mapping was obtained for the quantitative correspondence between the physical spatial length and the image pixel dimension. The optimal pruning points on each main branch were precisely located within the image plane. Experimental results demonstrate that the improved U-Net model exhibited superior segmentation performance compared with the rest advanced semantic segmentation models, such as the DeepLabV3+ and PSPNet. Specifically, there were the Mean Intersection over Union (MIoU) of 87.58%, the mean Pixel Accuracy (mPA) of 93.76%, and the Recall rate of 96.24%, indicating its robustness and effectiveness in accurately identifying and segmenting target features within the image data. Furthermore, the success rates to identify and locate the pruning points were 90.81% in direct light, 84.88% in backlight conditions, and 80.52% in cloudy conditions. At the same time, the localization success rate was 90%, and the average identification of a single branch took 1.93 s in the cutting point localization. This finding can also provide the technical support for the pile picking of the green Sichuan pepper harvesting using picking robots.

An accurate and rapid navigation is often required for the orchard robots in complex hilly and mountainous terrain, in order to advance the automation and intelligence of the cultivation. In this study, a path-planning approach was introduced for the orchard robots using aerial imagery and improved U-net semantic segmentation. Firstly, the drones were used to capture the aerial images of the orchard during the fertilization (April 2024), weeding (September 2024) and harvesting period (November 2024). Labelme was employed to annotate the fruit trees, flagstone road, and slabstone features in the images, in order to generate the mask images. An orchard-aerial dataset was obtained from the annotated images after data augmentation. Secondly, the improved U-net model was utilized to train the dataset, and then extract the critical orchard features—including the fruit trees, flagstone road, and drainage ditches—from the aerial images. The convolution structure of the encoder in the original model was retained to change the downsampling from the MaxPool to the MaxBlurPool. The loss of the fine details was minimized for the generalization of the model. The ReLU activation was replaced with the Swish, in order to maintain the gradient fluidity of the encoder, and then mitigate the vanishing-gradient issues. Thirdly, a receptive field block (RFB) was inserted at the final stage of the encoder, in order to obtain the receptive fields of the different sizes after the multi-scale convolution. As such, more diverse orchard-environment information was captured using the improved model. Finally, an SE (squeeze-and-excitation) attention mechanism was appended to every decoder block, in order to markedly predict the complex environment over all operational stages. The improved U-net was also applied to predict the key features—fruit trees, flagstone road, and stone slabs. The connected areas of the fruit trees were then divided into the single fruit tree ones after accurate identification, in order to facilitate the fruit tree positioning and path planning. The layer-by-layer opening operation was utilized as follows: 1) The connected component analysis was performed on the fruit tree images, in order to separate into the single- and multi-tree areas. Among them, the horizontal and vertical segmentations were applied into the multi-tree areas. After that, the multi-tree image was processed to merge with the unprocessed single-tree image, in order to obtain a new region of the fruit trees. The first layer of the segmentation was formed in the fruit tree after layer-by-layer segmentation. The second and subsequent layers (from layers 3 to 16) were followed the same procedure, until all the fruit tree regions were segmented into the single-tree regions. Drainage ditches were also inferred from the model-identified slabstone regions. 2) The single-tree areas, flagstone road, and drainage ditches were then classified into the passable and non-passable zones, in order to generate an orchard navigational map. The scale of the map was computed to relate the pixel spacing between slabstone and their measured physical distance. 3) An improved A* algorithm was applied to plan the path of the orchard map. The conventional unidirectional A* was replaced with a bidirectional variant accelerated planning. While a dynamic heuristic was improved the path accuracy along stone-slab roads. An orchard-specific turning-endpoint search was then used to identify the corner points, thereby ensuring that the planned route passed every fruit tree in the orchard. The results showed that the improved U-net was achieved in a mean intersection-over-union (mIoU) of 92.25%, thus outperforming the original U-Net, Res-U-Net, DeepLabV3+, and PSPNet by 2.34, 17.00, 7.83, and 19.11 percentage points, respectively. The mean pixel accuracy (MPA) of 95.72% was exceeded the original U-Net, Res-U-Net, DeepLabV3+, and PSPNet by 1.40, 15.76, 2.93, and 4.37 percentage points, respectively. The best performance was consistently achieved after training. In addition, the improved A* algorithm yielded a root-mean-square error of only 0.278–0.710 m relative to the actual optimal driving path of the orchard robot. And its mean path planning time was 36.87 s, which was 3.87, 6.21, and 6.41 s faster than the original A*, D*, and Dijkstra algorithm, respectively. The highly feasible approach can offer a practical reference for path planning in complex real-world orchards.

Feeding difficulty and easy clogging have confined the picking efficiency of green Sichuan pepper, leading to the “pile picking”. In this study, a rotating shear picking device was designed for green Sichuan pepper. Firstly, a picking scheme was determined, according to the physical and mechanical properties of prickly ash branches. The specific procedures included the rotating drive, guide feeding, and shearing picking of prickly ash branches. The reciprocating cutters were selected to realize the shear picking. The double-action reciprocating cutters were also designed for their transmission. The structure and motion parameters were determined to meet the requirements of rotary shear picking. Secondly, the simulation model was established to shear the prickly ash branch using ANSYS/LS-DYNA. The orthogonal tests were carried out with the cutting angle of the cutter tooth, the edge angle of the cutter tooth, and the thickness of the cutter tooth as the control factors, while the peak cutting force as the evaluation index. The results showed that the optimal tooth parameters were the cutting angle of 20 °, edge angle of 50°, and tooth thickness of 2.5 mm, with the peak cutting force 3.739 N, which was fully met the requirements of shear picking for the green Sichuan pepper. Finally, the prickly ash branches with fresh green Sichuan pepper were piled in the Sansheng Town, Beibei District, Chongqing, China, in June, 2023. A single-factor experiment was carried out with the branch feeding angle, branch feeding speed, and branch rotational speed as the control factors, while the picking efficiency, net collection rate, and fruit injury rate as the evaluation indices. The branch feeding angle, branch feeding speed, and branch rotation speed were determined in the range of 40°-60°, 20-40 mm/s, and 20-40 r/min, respectively. A three-factor quadratic regression orthogonal combination test was analyzed using Box-Behnken design on the Design-Expert 12. The optimal operating parameters were achieved in the branch feeding angle of 55.035°, the branch feeding speed of 32.04 mm/s, and the branch rotation speed of 29.635 r/min, in the rotating shear picking device for green Sichuan pepper. The performance was predicted as the picking efficiency of 10.78 kg/h, the net collection rate of 95.66%, and the fruit injury rate of 12.06%. The optimized parameters were rounded as the branch feeding angle of 55°, the branch feeding speed of 33.21 mm/s, and the branch rotational speed of 30r/min for the prototype test. Experiment validated that the average picking efficiency of a single person feeding a branch for the green Sichuan pepper was 10.95 kg/h using optimal operating parameters, with an average net collection rate of 95.57%, and an average fruit injury rate of 12.87%. The finding can provide the technical references to develop the picking machines for green Sichuan pepper.

As the cornerstone of agricultural production, soil provides various nutrients and moisture essential for crop growth, directly influencing crop yield and quality. Field soil information management constitutes a pivotal component in the development of modern agriculture. Currently, manual soil sampling coupled with offline parameter testing remains the primary approach in field management, which is plagued by issues such as high labor costs, intensive workload, and inefficiencies in collection and measurement. To mitigate these challenges and enhance the cost-effectiveness and efficiency of field management, a soil automatic sampling and real-timesoil parameter detection device was developed in this study. Furthermore, a prediction method based on the BPNN (Back Propagation Neural Network) is proposed for estimating soil firmness and mass moisture content. Firstly, based on the requirements for automatic soil sample collection and parameter measurement, a direct-pressure, two-stage, and step-by-step soil sampling and inspection mechanism, an unloading mechanism, and an indexing soil sample collection mechanism were designed. These mechanisms were analyzed and verified, resulting in the determination of a 400 mm movement stroke and a maximum soil penetration thrust of 800 N. A dual-layer control system architecture was also established, utilizing the Jetson TX2 embedded computer and STM32F3 series microcontroller, integrated with GNSS positioning. This system enabled functions such as automatic soil sampling, autonomous navigation, information recording and transmission, soil sampling self-protection, and dynamic prediction of soil firmness and mass moisture content. Secondly, a three-layer BPNN neural network prediction model was constructed to establish a regression relationship between easily measurable parameters including volumetric water content, soil sampling current, sampling speed, and sampling depth, with soil firmness and mass water content. The model was trained and tested using 275 experimental samples, resulting in an optimal number of hidden layer nodes determined as 10. The average percentage errors for the predictions of soil firmness and mass moisture content were 7.74% and 1.53%, respectively. Finally, to validate the machine's overall performance, field tests were conducted at 10 sampling points along an inspection path in an orange grove. The evaluation criteria included machine sampling time, soil penetration depth of the temperature and humidity sensor probe, absolute error of soil weight measurement, and relative errors of predicted soil firmness and mass moisture content. The results indicated that the average soil sampling time per operation was 60.5 s, the average soil penetration depth of the sensor probe was 64.7 mm, the average absolute error of soil sample weight measurement is 1.53 g. and the average relative errors of predicted soil firmness and mass moisture content at the 10 sampling points were 6.37% and 5.00%, respectively. These results satisfied the requirements for soil sampling and parameter detection. Additionally, by combining geographical location information, a field distribution map of soil firmness and mass water content was provided. This study provides a reference for intelligent soil collection, real-timeparameter detection, and visual management of field soil information distribution.