The study on the deposition efficiency of pesticide droplets on soybean leaves can provide the basis for reducing pesticide quantity and increasing pesticide efficiency during the application of soybean plant protection machinery. The movement behavior of droplet impinges on the plant leaf surface is affected by many factors, among which the most important and the easiest to adjust are spray droplet size and impingement velocity. By changing the droplet size and impact velocity and using Fluent simulation software, the pesticide droplet hitting the soybean leaf surface was simulated and a test platform was established to verify the simulation results. The conclusions are as follows: The longitudinal roughness of soybean leaves is higher than the transverse roughness, the longitudinal pressure of soybean leaves is higher than the transverse pressure during the impact process, and the velocity of droplet spreading along the longitudinal is lower than that of spreading along the transverse; although soybean leaf surface has high adhesion, droplet losses still exist when droplet impact velocity is relatively high. The maximum spreading diameter of the droplet increases first and then decreases with the increase of impact velocity. At the same time, the maximum spreading diameter of droplet increases with the increase of particle size. The droplet deposition was best at 1.34 m/s impact velocity and 985 μm particle size. This conclusion can provide optimal operation parameters for soybean plant protection operation which can be used to guide soybean plant protection operation, improve control effect, reduce quantity and increase efficiency.
Deep learning techniques have made great progress in the field of target detection in recent years, making it possible to accurately identify plants in complex environments in agricultural fields. This project combines deep learning algorithms with spraying technology to design a machine vision precision real-time targeting spraying system for field scenarios. Firstly, the overall structure scheme of the system consisting of image acquisition and recognition module, electronically controlled spray module and pressure-stabilized pesticide supply module was proposed. After that, based on the target detection model YOLOv5s, the model is lightened and improved by replacing the backbone network and adding an attention mechanism. Based on this, a grille decision control algorithm for solenoid valve group on-off was designed, while common malignant weeds were selected as objects to produce data sets and complete model training. Finally, the deployment of the hardware system and detection model on the electric spray bar sprayer was completed, and field trials were conducted at different speeds. The experimental results show that the improved algorithm reduces the model size to 53.57% of the original model with less impact on mAP accuracy, improves FPS by 18.16%. The accuracy of on-target spraying at 2km/h, 3km/h and 4km/h speeds were 90.80%, 86.20% and 79.61%, respectively, and the spraying hit rate decreased as the operating speed increased. Among the hit rate components, the effective recognition rate was significantly affected by speed, while the relative recognition hit rate was less affected.
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