To investigate an effective physical pest control method for tea trees, we designed and manufactured a suction-based pest-capture machine (hereafter labeled the “pest vacuum”) and conducted a test and field experiment to evaluate its effectiveness in the control of Empoasca vitis Göthe (E. vitis). Based on the proposed model, the minimum practical air velocity of the pest vacuum was 5.85 m s⁻¹. The field experiment included two treatments and a blank control. In treatment 1, we used the pest vacuum along tea trees only once, while in treatment 2, the pest vacuum was used along trees twice, separately, in 2 consecutive periods, and with an interval of more than 30 min. The results show that a highly significant difference arose among the dropping rates of the two treatments and the blank control instantly after suction, a significant difference among the dropping rates 7 days later, and no significant difference 14 days later. The dropping rate and the effective rate of prevention of treatment 2 reached 81.87% and 80.60% instantly after suction. Moreover, the effective rate of prevention of nymphs was slightly higher than that of adults. Overall, the pest vacuum had a prominent, albeit short-term, effect on the control of E. vitis. Seven days after the suction, it is better to repeat the suction five days after the first suction. The pest vacuum provides a valid physical method for pest control, so more attention should be paid in future investigations to reducing the weight and working noise of the pest vacuum.

Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the exploration phase to solve the problem of conflicting exploration strategies due to randomness. In addition, the salp swarm algorithm (SSA) with a nonlinear inertia weight is embedded into the HHO, such that the superior explorative ability of SSA can fill the gap in the exploration of HHO. Finally, the optimal solution is greedily selected between SSA-based individuals and HHO-based individuals. The effectiveness of the proposed improved HHO optimizer is checked through a comparison with other swarm intelligence methods in typical benchmark problems. Moreover, the experimental results of form deviation-zone evaluation on primitive geometries show that the improved method can accurately solve various form deviations, providing an effective general solution for primitive geometries in the manufacturing and metrology fields.

Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens.