EAIS-Former: An efficient and accurate image segmentation method for fruit leaf diseases

Fruit leaf disease segmentation is an essential foundation for achieving accurate disease diagnosis and identification. However, shadows caused by folded leaves and serrations on leaves can lead to difficulty in extracting edge features, affecting the accuracy of leaf segmentation. In addition, the varying shapes and blurred boundaries of disease spots can further lead to poor segmentation performance of spots. To address the above problems, this work proposes a method called EAIS-Former by combining the advantages of global modeling of Transformer, local processing and positional coding of convolutional neural network (CNN) for accurate segmentation in fruit leaf disease images. Dual scale overlap (DSO) patch embedding is designed to effectively extract multi-scale disease features by dual paths to alleviate omission of lesions. Ultra large convolution (ULC) Transformer block is customized for performing positional encoding and global modeling to efficiently extract global and positional features of leaves and diseases. Skip convolutional local optimization (SCLO) module is proposed to optimize the local detail and edge information and improve the pixel classification ability of the model so that the segmentation results of leaves and spots can be finer and more tiny spots can be extracted. Double layer upsampling (DLU) decoder is built to efficiently fuse the detail information with the semantic information and output the accurate segmentation results of leaves and spots. The experimental results show that the proposed method reach 99.04%, 98.64%, 99.24%, 99.42%, 98.59% and 98.58% intersection over union (IoU) for leaf segmentation on apple rust, pomegranate cercospora spot, mango anthracnose, jamun fungal disease, apple alternaria blotch and apple gray spot datasets, respectively. The IoU of lesion segmentation achieve 94.47%, 94.54%, 83.83%, 86.60%, 89.59% and 88.76%, respectively. In contrast to DeepLabv3+, the accuracy of disease segmentation is raised by 5.25%, 5.15%, 5.55%, 7.64%, 7.04% and 9.35%, respectively. Compared with U-Net, the proposed method improves the accuracy of disease spot segmentation by 4.3%, 4.44%, 5.26%, 9.42%, 5.87% and 6.53% under the six fruit leaf test sets, respectively. In addition, total parameters and FLOPs of the proposed method are only 18.44% and 8.47% of U-Net, respectively. Therefore, this study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation, which provides a sufficient basis for the accurate analysis of fruit leaves and diseases.