Variable rate air‐assisted spray based on real‐time disease spot identification

Abstract

Background Currently, the variable‐rate application (VA) of agrochemicals on fruit trees is based on canopy volume and biomass. The canopy volume has a significant relationship with disease resistance and degree of disease incidence. Therefore, this study proposes a VA method that uses deep convolutional neural networks for real‐time recognition of disease spots on pear trees. Furthermore, it specifies the limitations and application scenarios of the disease spot recognition. Field performance tests were conducted to verify the performance of the proposed VA system. Results The results showed a mean average precision, precision, and recall of 87.42%, 83.76%, and 87.23%, respectively. The spot recognition rate was 81.3% when the canopy sampling distance, spot diameter, and canopy porosity were 1.2 m, 4–8 mm, and 55.76%, respectively. The results indicate that the proposed VA system saved 51.9% spray volume compared to conventional methods while ensuring quality. Conclusion Compared to the traditional constant rate model, the proposed VA technology based on real‐time disease spot identification can reduce spraying in nondiseased areas, thereby abandoning the previous saturation application practice and significantly reducing pesticide use. © 2022 Society of Chemical Industry.

TSAF‐Net: a rotated two‐stage Cnaphalocrocis medinalis damage detection method based on anchor‐free arbitrary‐oriented proposal network

“…[23][24][25] Some researchers have done studies on pest and disease detection based on the YOLO series methods. [26][27][28][29] Jin et al 30 evaluate the performance of YOLOv3 and other CNNbased methods in detecting the vegetable crop. Yao et al 31 propose an improved RetinaNet model for automatic detection of pest symptoms in rice canopy.…”