Interpretable mango leaf disease detection using a hybrid CNN–transformer model with GLCM features
Year: 2025
Type: article
Abstract: Mango leaf diseases significantly hinder crop yield and food security in tropical regions, necessitating accurate and timely diagnostic tools. Traditional visual inspection methods are often subjective, time-consuming, and lack scalability, while existing deep learning approaches struggle with dataset imbalance, generalization limitations, and interpretability issues. To address these challenges, we propose ViX-MangoEFormer, a hybrid model that combines convolutional layers with self-attention mechanisms for robust classification of eight mango leaf conditions. The architecture incorporates MBConv4D and MBConv3D modules to capture both localized textures and global patterns, while GLCM-based statistical features enhance discriminatory power. Additionally, a stacking ensemble (MangoNet-Stack), comprising five pretrained models, is introduced as a comparative benchmark. Both models are trained and validated on a merged dataset of 25,530 images from four public sources, including balanced and imbalanced classes. Grad-CAM-based explainability is natively integrated to offer real-time visual rationales. Experimental results demonstrate that ViX-MangoEFormer achieves an F1 score of 99.78% and MCC of 99.34%, outperforming all baseline models. Furthermore, cross-domain tests reveal strong generalization to morphologically similar crops. A web application has been deployed to deliver real-time predictions with transparent explanations, providing an effective and interpretable solution for precision agriculture.
Institution: International American University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4410906506