Multidimensional Attention-Based CNN Model for Identifying Apple Leaf Disease

Abstract

To prevent the spread of illnesses and guarantee the steady and healthy growth of the apple sector, the proper diagnosis of apple leaf diseases is of utmost importance. The subtle interclass variations and enormous intraclass variances among apple leaf disease features, together with the uniformity of disease spots and the complicated background environment, make apple leaf disease diagnosis extremely challenging. A unique dual-branch apple leaf disease diagnosis system (DBNet) was put out to address the aforementioned issues. An attention branch with many dimensions and a multiscale joint branch (MS) make up the dual-branch network topology of the DBNet (DA). In this study, the MS branch and the DA branch are combined to create a DBNet, which successfully improves recognition accuracy while mitigating the negative impacts of complicated backdrop environments and lesion similarities. The accuracy of the DBNet network increases by 0.02843, 0.02412, 0.0144, and 0.0125, respectively, when compared to previous leaf disease detection models. This makes it evident that the suggested DBNet model has certain benefits over others in terms of identifying apple leaf disease.