A high-available segmentation algorithm for corn leaves and leaf spot disease based on feature fusion

Detection and identification of corn diseases are crucial for disease control, and the segmentation of corn disease leaf image is a key step to achieve this goal. However, the images of diseased leaves in real fields are usually very complex, with characteristics of irregular shapes, blurred boundaries and unsharp background, which poses great challenges to disease prevention. To address this issue, our team constructed a dataset of diseased leaves with 857 images. Additionally, this paper proposes a high-availability segmentation algorithm for corn leaves with leaf spot disease, called SEF-UNet, which uses Res-UNet as the backbone network. The algorithm references SElayer and ELA (Efficient Local Attention). Simultaneously,we implement a feature fusion network that focuses on the output of each layer. Experimental results indicate that the Mean Intersection over Union (mIOU),Mean Pixel Accuracy (mPA), Mean Precision (mPrecision), and Mean Recall (mRecall),metrics of SEF-UNet network reach 92.62%, 95.74%, 96.63% and 95.64%.We compared our proposed method with UNet, Res-UNet, PspNet, DeepLabv3+, DANet, CCNet, Segformer-b3, and SEF-UNet under the same experimental conditions. The results demonstrate that our method significantly improves the accuracy of diseased leaf image segmentation. It provides a reference method for disease monitoring, as well as a technical basis for assessing disease severity.