Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production.  Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.  Therefore, this paper proposes a novel method to identify banana leaf diseases.  First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images.  The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.  Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50.  In this, the Ghost Module is implemented to improve the network’s effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model’s computational speed is increased using the hybrid activation function of RReLU and Swish.  Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13,021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.