Multiscale Tea Disease Detection with Channel–Spatial Attention

Abstract

Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the backbone network and fuse these features through the neck for accurate disease detection. This paper proposes a novel tea disease detection method that enhances feature expression of the backbone network and the feature fusion capability of the neck: (1) constructing an inverted residual self-attention module as a backbone plugin to capture the long-distance dependencies of disease spots on the leaves; and (2) developing a channel–spatial attention module with residual connection in the neck network to enhance the contextual semantic information of fused features in disease images and eliminate complex background noise. For the second step, the proposed channel–spatial attention module uses Residual Channel Attention (RCA) to enhance inter-channel interactions, facilitating discrimination between disease spots and normal leaf regions, and employs spatial attention (SA) to enhance essential areas of tea diseases. Experimental results demonstrate that the proposed method achieved accuracy and mAP scores of 92.9% and 94.6%, respectively. In particular, this method demonstrated improvements of 6.4% in accuracy and 6.2% in mAP compared to the SSD model.