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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.