Design and trial of precision spraying system for weeds in winter wheat field at tillering stage

During the tillering stage of wheat, the distribution of weeds in the field is irregular, often showing single plants or clusters. Current precision spraying systems are mainly suitable for locating and spraying single-plant vegetation, which usually leads to the system missing or under-spraying when dealing with clustered weeds. In this study, a precision spraying control method is proposed to reduce the effect of camera frame rate on weed localization failure through three sets of position determination regions, and to address the effect of solenoid valve response frequency on precision spraying by controlling the spray nozzle to continuously spray herbicides on clustered weeds through a velocity-adaptive dynamic overlap region. To improve the accuracy of weed detection, GCGS-YOLO is proposed as a weed target detection model, and we integrate the Global Context (GC) attention mechanism with the traditional C3 module to optimize the backbone feature extraction network, and introduce the GSConv module to improve the neck network. The improved models P, R, mAP and F1 were 88 %, 84.6 %, 92.2 % and 86.3 %, which were 3 %, 3.1 %, 2.7 % and 3.1 % higher compared to the original model. The precision spraying algorithms and systems were integrated in a test bed and sprayer to carry out the tests. The tests showed that the recognition rate and spraying rate on the test bed could reach >98 % at different speeds. The results of the field test showed that the recognition rate and spray application rate of the sprayer were 91.2 % and 96.1 %, respectively, at a speed of 0.2 m/s. The research results can reduce the waste of herbicide, improve the efficiency of weeding, and provide reference for large-scale precision weeding.