Research on apple leaf disease segmentation method based on improved U-net

With the rapid development of deep learning theory in the field of image processing, its potential in the detection and recognition of agricultural diseases is gradually emerging. Given that apple leaf diseases are one of the common types of apple diseases that directly affect yield and quality, and considering the limitations of traditional segmentation methods, this paper proposes an apple leaf disease image segmentation method based on an improved U-net network. To overcome the deficiencies in detail capture and segmentation accuracy of existing methods, a pre-trained VGG16 network is introduced as a feature encoder, and an Enhanced Convolution Layer (EnhancedConvLayer) is proposed. The design of this layer includes parallel processing paths to fuse different feature information and incorporates the Convolutional Block Attention Module (CBAM), aiming to enhance the model's focus on key image features. Experimental results on the ATLDSD dataset show that the improved model achieves better Mean Intersection over Union (mIoU) and Mean Pixel Accuracy (MPA) than U-net, SegNet, and Unet++ in the detection of apple leaf diseases.