The effective quantification of deposition rate is of vital importance in optimizing the application performance and the utilization of pesticides; meanwhile, the canopies of fruit tree orchards are large, with dense branches and leaves shading each other, making it difficult to quantify spraying efficiency. Therefore, it is imperative to develop a facile methodology for assessing the performance of different spraying techniques in terms of distribution and utilization rate in orchards. To evaluate spraying efficacy in orchards, a canopy segmentation method was developed in to be able to determine the spray deposition rate. The distribution and deposition rate of spray liquid applied using three kinds of orchard sprayer were measured in a pear orchard and a peach orchard. The test results showed that the trailer sprayer had the highest deposition rates, with values of 31.54% and 56.92% on peach and pear trees, respectively. The deposition rates of the mounted sprayer in the peach and pear canopies were 21.75% and 40.61%, and the rates of the hand-held sprayer were 25.19% and 29.97%, respectively. The spray gun had the best droplet distribution uniformity, with CVs of the spray in the peach and pear canopies of 20.54% and 25.06%, respectively. The CVs in the peach and pear canopies were 35.98% and 26.54% for the trailer sprayer, and the CVs of the mounted sprayer were 92.52% and 94.90%, respectively. The canopy segmentation method could effectively be used to calculate the deposition rate and drioplet distribution in orchard application, while a great deal of time was consumed by counting the number of leaves in the different areas of the fruit tree canopies. Therefore, research on the density of branches and leaves in fruit tree canopies should be carried out in order to improve the efficiency of fruit tree canopy information extraction.

Thinning is an important agronomic process in pear production, thus the detection of pear inflorescence is an important technology for intelligentization of blossom thinning. In this paper, images of buds and flowers were collected under different natural conditions for model training, and the images were augmented by data augmentation methods. Model training was performed based on the YOLOv5s network with coordinate attention mechanism added to the backbone network and compared with the native YOLOv5s, YOLOv3, SSD 300, and Faster-RCNN algorithms. The mAP, F1 score and recall of the algorithm reached 93.32%, 91.10%, and 91.99%. The model size only took up 14.1 MB, and the average detection time was 27 ms, which are suitable for application in actual intelligent blossom thinning equipment.

To solve the problem of mechanized weeding in trunk type pear orchard, combined with the technology of stubble elimination and hydraulic obstacle avoidance, the authors designed an obstacle avoidance mower assembly. This paper carried out a field performance evaluation test to verify its usefulness. Six indexes were tested to evaluate its working performance. The results were: inter-row crushing rate of 89.99%, intra-row miss cutting rate of 2.42%, stubble stability coefficient variation of 4.25%, working efficiency of 0.32 hm2/h, fuel consumption of 16.25 L/hm2, profitable area of 0.75 hm2. The study could provide a reference for orchard mechanized weeding.