An assembly line of rice seedling sowing can be used to effectively enhance the production efficiency with less labor intensity in the better adaptability of seedlings for machine transplantation. However, manual delivery has been confined to the large-scale operation of the number of trays in the front-end tray-splitting device. The current splitting tray device cannot fully meet the requirements of high-speed operations in conjunction with the rice seedling sowing assembly line. In this study, a mechanical pneumatic automatic splitting tray device was designed to rapidly and accurately split and supply trays, with a production efficiency of at least 2 000 trays/h. The high-speed sowing was suitable to equip with the 2BP-2000 rice seedling planter. Three mechanisms consisted of a splitting tray, a conveyor, and a limit auxiliary, together with a control system. The tray paddle was indirectly lifted or lowered by the extension and contraction of the cylinder piston rod, enabling continuous trays splitting of stacked trays. The maximum number of trays per stack was 10 trays. The structure and motion parameters of the tray splitting mechanism were determined during operation. The conveyor mechanism was divided into the tray and its splitting sections. The motion model of the seedling tray conveying mechanism was established using theoretical analysis. The conveying speed of tray tray-splitting section was about 2.5 times that of the sowing one under different production rates. The seedling trays were then conveyed without spacing. The control system was designed with PLC as the core, in order to determine the wiring logic and control flow. A single-factor test was conducted to clarify the effect of different production rates on the stability of the chuck and tray-splitting device. An evaluation index was taken as the splitting tray stability. The results showed that the better stability of splitting tray was achieved in not less than 98.67% at a production rate of 1 600 to 2 000 trays/h. A three-factor and three-level orthogonal rotary test was conducted to determine the optimal parameter combinations of the automatic tray-splitting device during tray-splitting. The test factors included the number of stacked trays, productivity, and tray quality, with the success rate of tray-splitting as the test index. There was the greatest influence of productivity on the success rate, followed by the number of stacked trays and tray quality. The success rate of receiving trays decreased with the increasing productivity. The success rate also decreased gradually with an increase in the number of stacked trays. The optimal combination of parameters was obtained with a productivity of 2 000 trays/h, a number of stacked trays of 6 trays/stack, and a tray quality of 750 g/tray. The average measured success rate of the tray device tray was 98.43%, with a predicted difference of only 0.29 percentage points. This disc device presented better adaptability to the trays of different weights. The structure and timing control system fully met the requirements of the application. This finding can provide a practical application value in the mechanization level of hard tray seedlings during rice sowing.

Floating seedlings have been widely used in recent years, because of the small footprint, short nursery cycle, and high quality of pepper seedlings. The seeding link of pepper floating seedling is required for one hole and one-grain precision seeding. Among them, manual spot seeding can usually sow with high labor intensity and low seeding efficiency. The high seeding quality is confined to the complex and costly structure of the pneumatic precision seed-metering device. While the mechanical seed-metering device is more suitable for the round or pelletized vegetable seeds, rather than the small pepper seeds. In this study, a low-cost, lightweight, and simplified mechatronic precision seed-metering device was designed for the pepper floating seedlings, according to the magnetic return-type seed-metering device. A series of mechanisms were designed for the reciprocating motion, rotating, and seed dropping. The seed metering device was improved from the filling and discharging of magnetic return-type seed-metering device by means of various mechanisms. The important factors were selected as the diameter of the filling hole and the depth of the seed-dropping hole in the magnetic return-type seed-metering device. The single grain rate was improved for the high quality of sowing. The triaxial dimensions of the pepper seeds were then measured to determine the size of the filling and dropping holes. In reciprocating motion, the crank radius and seed displacement were determined for the minimum radius of the crank. In rotating, the minimum torque of the motor was determined by the center of mass and force analysis. The influencing factors were selected as crank diameter, crank motor speed, rotating motor angular speed, rotation angle, and seed volume in the trial test. One-factor test was carried out to obtain better ranges in the crank diameter, crank motor speed, rotating motor angular speed, rotation angle, and seed volume. Among them, the crank diameter, crank motor speed, and seed volume shared a significant influence on the performance of the seed-metering device. A three-factor and three-level orthogonal test was conducted to establish a regression model, where the crank diameter, crank motor speed, and seed volume were taken as test factors, while the target variables were the single-seed qualified, reseeding, and missed seeding indexes. The bench test was also carried out to verify the model under the optimal combination of parameters. The test results show that the crank diameter of 30 mm, crank motor speed of 230 r/min, and seed volume of 4 000 grains under the rotation angle of 30° and rotating motor angular speed of 0.07 rad/s (i.e., productivity of 240 trays/h). The seed-metering device performed better with a single-seed qualified index of 91.04%, a reseeding index of 5.21%, and a missed seeding index of 3.75%. The seeding performance fully met the industrial requirements of floating seedlings in pepper production and small-seeded seed-metering devices.

White radish has been one of the most popular root vegetables in recent years. The mechanized production of white radish often includes the ploughing, planting, managing, cleaning, and post-processing of mature products. Among them, the harvesting has been the weakest degree of mechanization in the production. The "Picking type" harvesting can serve as the joint harvesting for the white radish. The tassel can also be operated to realize the clamping, conveying, and tassel-cutting processes. Therefore, the gathering quality of the tassel can directly determine the overall harvesting. However, only a few reports focused on the tassel gathering of white radish. In this study, a tassel-gathering mechanism was designed for the white radish, according to the tassel's physical properties and fracture mechanics. The complete gathering of tassels was achieved in the highly qualified rate of cutting head with the low loss and damage rate in the process of machine harvesting. The gathering of white radish tassels into bundles was realized from the natural "semi-prostrate" state in the field, particularly for the operation of the subsequent links. A kinematic model of tassel gathering mechanism was constructed to propose the concept of "tassel-gathering speed ratio". The optimal range was learned during mathematical modeling; The mathematical model was constructed to explore the influence of structural and key motion parameters (such as the forward speed, tassel gathering speed ratio, and the installation angle of the tassel gathering mechanism) on the tassel performance. The mathematical model was then used to determine the performance of tassel gathering. The optimal structure and motion parameters of the tasseled gathering mechanism were solved to consider the complexity of the field environment and the unfavorable control of variables in the test. An indoor bench test was carried out to combine with the theoretical analysis. The influenced factors were taken as the forward speed, tassel gathering speed ratio, and installation angle of the tassel gathering mechanism of the white radish combine harvester. The success and breakage rate of tassel gathering were taken as the evaluation indexes. The three-factor and three-level response surface test was carried out using Design-Expert software. The regression mathematical model was established between the influenced factors and the indexes. The parameters of the model were optimized at the same time. The test results showed that the significance of the success rate of tassel gathering was ranked in the descending order of the forward speed, the tassel gathering speed ratio, and the installation angle of the tassel gathering mechanism; The significance of the breakage rate of tassel gathering was ranked in the descending order of the tassel gathering speed ratio, installation angle of tassel gathering mechanism, and forward speed. The optimal combination of parameters was the forward speed of 0.4 m/s, tassel gathering speed ratio of 3.9, and the installation angle of the tassel gathering mechanism of 86.3°. The success and breakage rate of tassel gathering were predicted as 94.56% and 10%, respectively. Experimental verification showed that the success and the breakage rate of tassel gathering were 92.04% and 8.81%, respectively, under the optimal combination of parameters. The evaluation indexes were close to the prediction, which fully met the demand of tassel gathering of picking-type combine harvester for the white radish. This finding can also provide a strong reference to optimize the white radish harvester machinery.

Chili pepper is one of the most widely planted vegetables in China. The current production of fresh chili peppers, such as field management and harvesting, faces the challenges of high labor intensity and low efficiency. The chili pepper industry is ever transitioning towards mechanization and intelligent production. The rapid and accurate detection of chili fruits in the natural environment is of great significance for the automatic picking of chili peppers. However, it is still lacking in the adaptive ability and detection accuracy of the model under different light and occlusion conditions. In this study, an improved detection model, called YOLOX_Pepper, was proposed for chili fruit using YOLOX. Firstly, a fusion-efficient channel CA (coordinate attention) attention mechanism was added to the YOLOX feature fusion network, in order to capture the key features of chili fruits. Secondly, the convolution module in the feature fusion module of the backbone network was replaced with Deformable Convolutional DCNv2 (Deformable ConvNets v2), in order to improve the perceptual ability of the model in the case of the complex geometric features of chili pepper length, width, and aspect ratio, due to branch and fruit occlusion. The experimental results showed that the improved YOLOX_Pepper model had mAP (mean average precision) of 93.30%, which was 3.99, 1.58, 3.19, and 2.84 percentage points higher than that of Faster R-CNN, YOLOv5, YOLOv7, and YOLOX, respectively, with an F1 score of 96%. Under strong light conditions, the mAP of green and red chili fruits of the YOLOX_Pepper model was 69.16% and 89.67%, respectively, and the number of correctly detected green and red peppers was 83 and 304, respectively. Under shadow conditions, the mAP of green and red peppers of the YOLOX_Pepper model was 77.21% and 90.42%, respectively, and the number of green and red peppers was 119 and 255 correctly detected. Under the lack of light conditions, the mAP of the YOLOX_Pepper model for green peppers and red peppers were 77.38% and 75.47%, respectively, and the number of correctly detected green and red peppers were 86 and 311, respectively. The YOLOX_Pepper model performed better in various light conditions, especially in the number and accuracy of detections, compared with the YOLOV5, YOLOV7, and YOLOX models. Under fruit occlusion conditions, the mAP of YOLOX_Pepper was 71.15% and 94.87% for green and red peppers, respectively, and the number of correct detections was 79 and 650 for green and red peppers, respectively. Under branch and foliage occlusion conditions, the mAP of YOLOX_Pepper was 83.98% and 87.10% for green and red peppers, respectively, and the number of correctly detected green and red peppers was 88 and 394, respectively. The improved YOLOX_Pepper model performed better in the chili fruit detection under different occlusions, compared with the YOLOv5, YOLOv7, and YOLOX models. The YOLOX_Pepper model showed excellent performance of detection in complex environments. The effectiveness of the improved module can also provide the intelligent production of chili peppers with reliable technical support.