An improved DSCCA-UNet for apple leaf disease severity estimation and prescription map generation

This paper investigates the problems of disease severity estimation and prescription map generation. By introducing dynamic snake convolution (DSC), a DSCC multi-scale feature extraction module is designed to build a new UNet architecture. The combination of DSCC module with VGG16 backbone can enhance the receptive field for segmented edge feature information which can achieve more detailed edge feature fusion. The channel attention (CA) and spatial attention (SA) in the CBAM attention module are disassembled and used in the skip connection part and the upsampling part, respectively. This new connection can obtain more location information of the spots and mitigate the effect of background on network learning. Moreover, an automatic pixel counting algorithm based on the improved DSCCA-UNet is designed to estimate the disease severity. Finally, the system of the apple leaf disease severity estimation and the variable prescription maps are obtained based on the PyQt5 tool and ArcGIS component. The experimental results show that the improved DSCCA-UNet model outper-forms other mainstream semantic segmentation models. It can more effectively complete the tasks of disease severity estimation and prescription map generation in actual orchard scenarios.