A hedgerow is one type of conservation buffer in grape cultivation. Mechanical harvesting has been widely used for the hedgerow-type grapes. However, it is often required to locate the peduncles of the grapes under the vines and leaves occlusion during harvesting. In this study, a leaf-removing mechanism was proposed to push away the obscuring vines, followed by the harvesting of the grape bunches. A leaf-removing harvesting robot was also developed using collaborative robotic arms. The efficient and low-damage harvesting of the grapes was achieved in complex occlusion scenarios. Firstly, a quantitative discrimination model was constructed for the peduncle visibility. The relative length, relative direction, and continuity were integrated to determine a visibility coefficient in the 0-1 interval. The occlusion degree of the peduncles was assessed in real time, including high, medium, and low visibility. In the grape peduncles with medium to low visibility, the optimal intervention point was identified to remove the occlusions by vines and leaves. Secondly, a spatial quadrilateral was constructed according to the endpoints of the grape peduncle and the occluding branch. The limited-memory broyden-fletcher-goldfarb-shanno was employed for the fermat-torricelli point as the optimal intervention point. Furthermore, a nonlinear mapping model was constructed from the end cartesian space to the joint space, in order to simplify the inverse solution of the robotic arm. The joint angle of the robotic arm was obtained corresponding to the end pose. The independent harvesting was achieved by the grape harvesting arm for the grapes with the highly visible peduncles, and the leaf-removing harvesting for the grapes with the medium to low visibility peduncles. Finally, the quantitative discrimination of the peduncle visibility was performed on 100 groups of samples. The results showed that the better performance was achieved in a visibility discrimination accuracy of 91.0% and a Kappa coefficient of 0.9. Among them, the discrimination accuracy for the high visibility was 94.1%. Field test results indicated that the harvesting damage rate was below 10.0% for the grapes with the highly visible peduncles, the success rate was 70.0%, and the average single-arm harvesting time was 3.2 s per cluster. While the occlusion handling mechanism was adopted in the grapes with the medium to low visibility peduncles. In the grapes with the medium visibility peduncles, the harvesting damage rate was not more than 23.3%, the success rate was 53.3%, and the average leaf-removing harvesting time was 8.7 s. In the grapes with the low visibility peduncles, the harvesting damage rate was below 36.6%, the success rate was 40.0%, and the average leaf-removing harvesting time was 14.8 s per cluster. The occlusion handling mechanism effectively distinguished the peduncles with the different occlusion degrees, and then switched the operating modes, indicating a relatively high harvesting success rate. The efficient, low-damage harvesting of the grapes was fully met under complex occlusion conditions in the hedgerow-type vineyards. The finding can also provide a strong reference for the mechanical harvesting of the hedgerow-type grapes.

Accurate and rapid wheat morphology reconstruction and trait collection are essential for selecting varieties, scientific cultivation, and precise management. A single perspective is limited by environmental obstructions, hindering the collection of high-throughput phenotype data for wheat plants. Therefore, a rapid reconstruction method of multi-view three-dimensional point cloud is proposed to realize the high-throughput and accurate identification of wheat phenotype. Firstly, taking wheat at the tillering stage as the experimental object, a multi-view acquisition system based on a RealSense sensor was constructed, and the point cloud data of wheat were obtained from 16 views. Secondly, a joint photometric and geometric objective was optimized, and space location was registered by colored Point Cloud Registration (colored) and Iterative Closest Point (ICP) algorithms. Furthermore, the Multiple View Stereo (MVS) algorithm was used to combine the depth image, RGB image, and spatial position obtained by coarse registration to enable the fine registration of multi-viewpoint clouds. Compared with the traditional Structure From Motion (SFM)-MVS algorithm, our proposed method is much faster, with an average reconstruction time of 33.82 s. Moreover, the wheat plant height, leaf length, leaf width, leaf area, and leaf angle of wheat were calculated based on the three-dimensional point cloud of the wheat plant. The experimental results showed that the determination coefficients of the method are 0.996, 0.958, 0.956, 0.984, and 0.849, respectively. Finally, phenotypic information such as compact degree, convex hull volume, and average leaf area of different wheat varieties was analyzed and identified, proving that the method could capture the phenotypic differences between varieties and individuals. The proposed method provides a rapid approach to quantify wheat phenotypic traits, aiding breeding, scientific cultivation, and environmental management.

Small arch sheds have been widely constructed in manual in China at present, particularly with the high labor intensity and low efficiency. The current commercial machinery of arch shed is the traction type in the form of shutdown insertion during construction. It is a high demand to improve the efficiency in the operation of shutdown insertion for the construction machinery. In this study, a continuous cuttage device was developed in the self-propelled shed machine for arch building. Specifically, the rear motor of the vehicle was used to drive forward, where the rear wheel drove the continuous insertion device to rotate via the transmission device, thereby driving the continuous insertion device to work. According to the agronomic requirements and overall structure, the key components of the continuous insertion device were determined to optimize the parameters of reverse transmission mechanism and bending components of the planetary gear train using simulation. The continuous and stable insertion operation was achieved to reduce the machine wear that caused by shutdown insertion for the high efficiency of arch construction. The reverse transmission mechanism and bending components of the planetary gear train were then designed to drive the bending components for the rotation and actions, such as bending and pressing poles. The soil resistance of shed pole was set as 51 N, when the maximum depth of insertion into the soil was 10 cm, according to the discrete element simulation of "Shed Pole-Soil". The stress simulation was conducted on the pressure pole arm of the shed pole and bending components, in order to ensure the successful insertion of the shed pole into the soil. The simulation results showed that the friction force between the pressure pole arm and the shed pole was 168 N, which was much greater than the soil resistance suffered by the shed pole. Moreover, the stress of shed pole was within the yield strength at the same time. The trajectory of bending pressure plate was analyzed in the bending components, according to the agronomic requirements of the depth and spacing of the small arch shed insertion. The optimal length of bending bracket was determined to be 27 cm, and the ratio of the speed of the central rotating shaft to the travel speed of vehicle was 0.85. The field trail results showed that the average insertion depth and spacing of shed pole were 7.04 and 74.11 cm, respectively, where the average error were 0.09 and 0.16 cm respectively. Therefore, the continuous insertion device of the self-propelled arch shed machine was developed to determine the optimal parameters of planetary gear train reverse transmission mechanism and bent parts using simulation. The continuous and stable insertion can be expected to reduce the wear of the machine that caused by the shutdown of the arch frame, particularly for the high construction efficiency of arch shed.