Abstract
<b>Abstract.</b> In many regions of the world, apple trees are heavily affected by Alternaria leaf blotch disease, causing severe losses in fruit yield and quality. The breeding of disease-resistant varieties is one of the means to mitigate the impact of this disease, but assessing the impact of leaves on the disease requires precise identification of the diseased area. Traditional detection methods are time-consuming, labor-intensive and expensive. An identification method that can quickly capture images of apple leaves and their diseased areas would greatly benefit the apple breeding. In this study, the pyramid scene parsing network combined with the convolutional block attention module (PSPNet-CBAM) can accurately identify apple leaves and segment disease spots, with MIoU of 95.86% and 89.24%, respectively. This study demonstrated the feasibility of rapid detection of PSPNet-CBAM to rapidly detect apple Alternaria leaf spot.
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