A ramie stripping machine has been commonly used in modern agriculture in recent years. However, the current manual feeding and back-pulling cannot fully meet the large-scale production, due to the high operational intensity, low safety, and unstable stripping quality. In this study, a double-drum stripping device was designed to replace the manual feeding and reverse-pulling in an automatic ramie fiber stripping machine. There were also the different mechanical properties between the ramie fiber layer and the woody part. Firstly, some measurements were performed on the physical dimensions and mechanical property parameters of "Chuanzhu No. 11" ramie stalks. Then, the technical solution of a double clamping mechanism was proposed with the synchronous belt clamping and conveying, motor-driven clamping mechanism flipping and changing position, and double-drum reverse stripping. The ramie stalks were fed directly, and then stripped at the base and tip of the ramie stalks in turn by the changing position of the clamps. The toothed rubber clamping plate was used to hold the stalks. The grooves and projections of the toothed rubber plate were firmly held to prevent the stalks crushed by the clamps and slipping out. The operation of the ramie stripping machine was divided into four steps: the straw feeding, base stripping, tip stripping, and collecting ramie skin. The key components mainly included the transposition clamping, lateral feeding, and ramie stripping device. The mechanical analysis of double-drum ramie stalk stripping showed that the stripping force was closely related to the drum speed. A mechanical test was carried out to investigate the relationship between the stalk feeding angle and stripping force. The results showed that the stalk feeding angle and stripping force shared a highly significant negative linear correlation. The structural design and theoretical analysis were performed on the main components in the ramie stripping device and clamping mechanism. After that, the structure and motion parameters of the ramie stripping machine were determined, including drum speed, reverse-pulling speed, and feeding angle. Furthermore, a single-factor simulation model was established using ANSYS/LS-DYNA to simulate the process of stripping and the amount of xylem removal, the loss of the bast fiber layer, and the force of the feeding direction. Previously, the ramie stalk actual stripping process was analyzed and a simulation model was developed. The structure of the ramie stalk was also analyzed during this time. The optimal ranges of parameters were achieved for a better stripping effectiveness: the roller speed of 350-650 r/min, the reverse-pulling speed of 0.2-0.4 m/s, and the feeding angle of 5°-15°. A three-factor and three-level orthogonal test was conducted, according to the Box-Behnken method. The results showed that the drum speed, reverse-pulling speed, and feeding angle posed the significant effects on the fiber percentage of fresh stalk, and the impurity rate of raw fiber using ANOVA and response surface analysis. In addition, a significant coupling effect was found in the interaction of drum speed and reverse-pulling speed, as well as each experimental factor, but there was no a simple linear relationship. The optimal operating parameters were obtained for the ramie stripping machine with the reverse-pulling clamping: the stripping drum speed of 450 r/min, the reverse-pulling speed of 0.32 m/s, and the feeding angle of 11°. Finally, the validation test of the ramie stripping machine prototype was carried out using the optimized parameters. A better performance was achieved in an average fresh stem fiber yield of 5.03% and an average impurity rate of 1.14% of raw ramie, fully meeting the national technical standards of the ramie stripping machine. The transposition clamping device can be expected to realize the ramie clamping and feeding, as well as the automatic reverse pulling. The simple and optimal structure of the ramie stripping machine was had improved the performance and quality of ramie stripping. The finding can provide a technical reference for the development of an automatic lightweight ramie stripping machine.

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.

China is a major citrus producer in the world. In the southern regions of our country, citrus is the fruit with the largest planting area and the most important economic status. However, citrus picking faces challenges like branch shading, difficulty locating fruit stalks, and varying stalk angles, causing low success rates and efficiency with traditional end effectors. This study takes navel oranges as the research object. This paper proposes a new type of end effector for fruit picking that mimics the manipulation of citrus fruits by fingers and passively cuts the fruit stalks. First, the physical parameters of the navel orange, such as its size, the diameter of the fruit stalk and the inclination angle, are determined. Then, the mechanical parameters such as the shear force and Young's modulus of the navel orange fruit stem are measured using the electronic universal testing machine. Based on the measurement data, the overall structure of the three-finger configuration end effector composed of two V-shaped cutting blades combined side by side is designed. The transmission mechanism of the crank-slider-rocker connecting rod mechanism is adopted to drive the reciprocating motion of the fingers, thereby prodding the citrus fruits and cutting the fruit stalks to achieve the picking function. For the design of the V-shaped cutting device, parameters such as the installation inclination angle of the V-shaped cutting blade, the opening angle of the blade, and the peak force range are determined through theoretical calculation. The peak cutting force under different included angle conditions is simulated and verified by using Ansys software. The simulation results show that the peak cutting force of the V-shaped cutting blade is the smallest when the included angle is 10° and the inclination angle is 6°. For the crank-slider-rocker transmission mechanism, its structural parameters are designed through theoretical calculation. Kinematic simulation analysis is carried out through Adams software, and it is determined that the transmission mechanism has a sharp rotation characteristic during the process of cutting fruit stalks. Finally, outdoor orchard picking tests are conducted on the trial-produced new type of end effector. The success rate of picking is 95.38%, the damage rate of navel oranges is 0, and the picking efficiency is 6.50 seconds pel orange. Meanwhile, a two-factor three-level orthogonal experiment is conducted on the citrus picking operation parameters and the post-harvest fruit stalk length. The variance analysis of the test data are performed using the Design-Expert software. The result shows that when the motor speed is 30 r/min、minhe post-harvest fruit stalk length of citrus is the shortest, only 19.8mm. This end effector can achieve rapid and non-destructive fruit picking under complex conditions where branches and leaves in the orchard are blocked, improving the success rate and efficiency of fruit picking. The research results can provide technical references for the development of the end effector of the picking robot.

Navel orange is a widely cultivated variety of citrus in southern China. The annual output of citrus has exceeded 60 million tons, indicating the largest producer of citrus with planting area and output ranking first in the world in recent years. However, the damage to navel oranges has been caused by external compression during harvesting and transportation. In this study, an experimental test was conducted on the compression deformation of navel oranges. A systematic analysis was implemented to explore the influence of different compression scales on the damage degree of navel oranges. Some parameters were identified for the damage size of navel oranges. Firstly, the mechanical properties of navel orange were tested by an electronic universal testing machine. The key mechanical parameters of navel orange peel and pulp were measured, such as Young's modulus, yield strength, and Poisson's ratio. The transverse, longitudinal, and oblique compressive strength tests of navel orange were carried out on the load-displacement curves to calculate the ultimate load. The test results showed that the mean compressive limit load of navel orange in the transverse direction was lower than that in the longitudinal and oblique directions under the same deformation. Meanwhile, the navel orange was in the elastic deformation stage, when the compression displacement ranged from 0 to 7.5 mm. Once exceeding this range, the navel orange was in the plastic deformation stage. The damage levels of navel orange were determined under different compression displacements. The compression recovery coefficients of navel orange were obtained to measure the size change of navel orange before and after transverse compression. The microstructure change of the navel orange peel was observed by the paraffin section. At the same time, the navel orange samples after the compression test were stored at room temperature and dark environment. The mass-loss rate was measured regularly to evaluate the damage degree of navel orange under different compression conditions from macro and micro perspectives. The results showed that the fruit compression recovery coefficients fluctuated greatly between 10 and 12.5 mm when the compression load was applied to the navel orange. When the compression level was not more than 10 mm, the fruit compression recovery coefficient was close to 0.75. The peel oil cells were intact, indicating almost no damage to the navel orange fruit. Once the compression level reached 12.5 mm, the compression recovery coefficient of the navel orange was significantly reduced, indicating the outstandingly broken peel oil cell. Finally, the three-dimensional solid model of navel orange peel and pulp was constructed by 3D scanner and reverse engineering. The stress distribution of the whole navel orange peel and pulp was also simulated by ANSYS/LS-DYNA. The simulation results showed that the stress of the flesh tissue at a 12.5 mm compression level was very close to the ultimate yield stress. The external load first caused irreversible plastic deformation of the flesh tissue, leading to mechanical damage to the navel orange. The simulation results were consistent with the compression test, which verified the measured mechanical parameters of the navel orange. Therefore, the extrusion deformation range of navel orange should be controlled within 10 mm and the external load should not exceed 63.24 N in the process of low-loss harvesting and transportation. The findings can also provide a strong reference for the loss reduction and postharvest storage of navel oranges during harvesting and transportation.