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

Abstract

Abstract 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.

Plant phenotyping relevance

リンゴ葉の病斑を画像分割・画素計数し、植物体の病害重症度を推定する手法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper uses the public ATLDSD apple leaf disease dataset (a cited prior-work dataset, not a paper-specific deposit) and reports no authors' public code, model, or data repository. The data availability statement only offers data on request, so any paper-specific assets require contacting the authors.

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