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Mulberry leaves serve as the primary food source for Bombyx mori silkworms, crucial for silk thread production. However, mulberry trees are highly susceptible to diseases, spreading rapidly and causing significant losses. Manual disease identification across large farms is arduous and time-consuming. Leveraging computer vision for early disease detection and classification can mitigate up to 90% of production losses. This study collected leaves from two regions of Bangladesh, categorized as healthy, leaf rust-affected, and leaf spot-affected. With a total of 1091 images, split into training (764), testing (218), and validation (109) sets for 5-fold cross-validation, preprocessing and augmentation yielded 6,000 images, including synthetics. This study compares ResNet50, VGG19, and MobileNetV3Small on a specific task following architecture modifications. Four convolutional layers with different output channels (512, 128, 64, and 32) were added to baseline models. We assessed how these architectural changes affected model correctness, computing efficiency, and convergence rates. Comparing three pretrained convolutional neural networks (CNNs) - MobileNetV3Small, ResNet50, and VGG19 - augmented with four additional layers, the modified MobileNetV3Small excelled in precision, recall, F1-score, and accuracy, achieving notable results of 97.0%, 96.4%, 96.4%, and 96.4%, respectively, across cross-validation folds. An efficient smartphone application employing the proposed model for mulberry leaf disease recognition was developed. Overall, the model outperformed existing State of the Art (SOTA) approaches, showcasing its effectiveness in disease identification. The interpretative Grad-CAM visualization images match sericulture specialists’ assessments, validating the model’s predictions. These results imply that, this eXplainable AI (XAI) approach with a modified deep learning architecture can appropriately classify mulberry leaves.

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world’s raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for three-class classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models’ findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDS-CNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves.