Simultaneous multi-disease detection from the same leaf: a generalized approach using deep learning and image splitting

Plant disease represents one of the most severe threats to agricultural production. Deep learning has emerged as a promising solution for automating the recognition of these diseases, leading to a richness of disease recognition applications based on deep learning. However, most existing applications do not address the challenge of simultaneous multi-disease detection from the same leaf. In this study, we introduce a deep learning-based model designed to detect and recognize multiple diseases from the same leaf simultaneously. Our method enables the recognition of each disease's symptoms separately from small leaf regions, independent of other diseases or specific crop types, through an isolation method. This approach also allows the model to generalize disease detection to new crops not encountered during training. Additionally, our method calculates the prevalence rate of each disease on the leaf and determines the overall extent of all diseases present. To evaluate the effectiveness of our approach, we applied it to the widely recognized PlantVillage dataset, creating a new version for training and testing with three CNN models: Small Inception, MiniVGGNet, and LeNet5. The results demonstrate that the Small Inception architecture outperformed the other two CNNs in terms of classification performance. Despite some class imbalances in the new dataset, which were addressed through the use of class weights, this approach significantly enhanced the model's performance. Furthermore, while the proposed method demonstrates high performance in controlled environments, though its consistency under real field conditions still warrants deeper investigation. Overall, the findings underscore the effectiveness of our method and highlight its potential as an efficient solution applicable across diverse agricultural contexts.