Inferior bowl seedlings have posed a great threat to the survival rate of later seedling transplantation in greenhouse hole tray seedling cultivation. It is urgent to remove the matrix blocks of these holes before leaving the factory. However, the existing mechanical removal often causes the particle scattering and omission. Air suction removal can be expected to effectively compensate for this defect. In this study, a series of experiments were conducted to optimize and then verify the structural parameters in the matrix air suction removal scheme. A systematic analysis was implemented to determine the relationship between pipeline design and adsorption efficiency under negative pressure. A single factor experiment was also carried out to preliminarily select the optimal values of pipe diameter (10, 18, 26, and 32 mm), pipe spacing (0, 4, 8, and 12 mm), and pipe length (1000, 2000, 3000, and 4000 mm). A optimal combination was obtained with the pipe diameter 26 mm, pipe spacing 6 mm, and pipe length 2000 mm. Further expansion was carried out with 200-hole potted seedlings as the research object (The seedling emergence rate of the entire tray was 92%, and the health rate was 86%). Four factors were performed on the substrate removal rate, including pipe type (round tube, flat tube, and square tube), pipe diameter (inner diameter of 23, 26, and 29 mm), pipe spacing (2, 6, and 10 mm), and pipe length (1000, 2000, and 3000 mm). Response surface analysis was conducted as well. The experimental results show that all four factors posed an impact on the matrix removel rate. Firstly, the pipe spacing and the pip type shared the greatest impact on the matrix removel rate, and then the pipe diameter. Nevertheless, there was the little effect of pipe length on the cleaning. Reasonable scaling was carried out in the later experimental design. Secondly, the larger the pipe diameter was, the better performance was. It was also necessary to consider the limitation of hole space and the damage to adjacent hole healthy seedlings. The smaller the pipe spacing was, the greater the negative pressure capacity was, and the better the matrix removel rate was, but the suction cup phenomenon was more serious. There was the little impact of the pipe length on the overall suction, but it cannot be ignored. Finally, the optimal combination of various factors was achieved: a square tube, an inner diameter of 26 mm at the end of the straw, a distance of 3 mm from the end of the straw to the port on the hole, and a length of approximately 1000 mm. Consequently, 95% matrix removal was achieved at the maximum negative pressure of 22 kPa,which is an average increase of 2.94% compared to before optimization. More efficient substrate removal was achieved under the same negative pressure. The better performance was gained than the mechanical removal equipment of low-quality bowl seedling substrate. This finding and also provide the data reference for the design and development of removal equipment with substrate negative pressure.

In China, tea products made from fresh leaves characterized by one leaf with one bud (1L1B) are classified as “Famous Tea”, which has better taste and higher economic value, but suffers from a labor shortage. Aiming at picking automation, existing studies focus on visual detection of 1L1B, but algorithm validation is limited to a specific variety of tea sprouting in a certain harvest season at a certain location, which limits the engineering application of developed tea picking robots working in various natural tea fields. To address this gap, a deep learning model DMT (detecting multispecies of tea) based on YOLOX-S was proposed in this paper. The DMT network takes YOLOX-S as a baseline and adds ECA-Net to the CSP Darknet and FPN of YOLOX-S. The average precision (AP), precision, and recall of DMT are 94.23%, 93.39%, and 88.02%, respectively, for detecting 1L1B sprouting in spring; 93.92%, 93.56%, and 87.88%, respectively, for detecting 1L1Bsprouting in autumn. These experimental results are better than those of the five current object detection models. After fine-tuning the DMT network with another dataset composed of multiple tea varieties, the DMT network can detect 1L1B for different varieties of tea in multiple picking seasons. The results can promote the engineering application of picking automation of fresh tea leaves.