ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence
Mango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretab...
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2025-05-01
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| author | Abdullah Al Noman Amira Hossain Anamul Sakib Jesika Debnath Hasib Fardin Abdullah Al Sakib Rezaul Haque Md. Redwan Ahmed Ahmed Wasif Reza M. Ali Akber Dewan |
| author_facet | Abdullah Al Noman Amira Hossain Anamul Sakib Jesika Debnath Hasib Fardin Abdullah Al Sakib Rezaul Haque Md. Redwan Ahmed Ahmed Wasif Reza M. Ali Akber Dewan |
| author_sort | Abdullah Al Noman |
| collection | DOAJ |
| description | Mango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretability. To address these challenges, this study introduces the ViX-MangoEFormer, which combines convolutional kernels and self-attention to effectively diagnose multiple mango leaf conditions in both balanced and imbalanced image sets. To benchmark against ViX-MangoEFormer, we developed a stacking ensemble model (MangoNet-Stack) that utilizes five transfer learning networks as base learners. All models were trained with Grad-CAM produced pixel-level explanations. In a combined dataset of 25,530 images, ViX-MangoEFormer achieved an F1 score of 99.78% and a Matthews Correlation Coefficient (MCC) of 99.34%. This performance consistently outperformed individual pre-trained models and MangoNet-Stack. Additionally, data augmentation has improved the performance of every architecture compared to its non-augmented version. Cross-domain tests on morphologically similar crop leaves confirmed strong generalization. Our findings validate the effectiveness of transformer attention and XAI in mango leaf disease detection. ViX-MangoEFormer is deployed as a web application that delivers real-time predictions, probability scores, and visual rationales. The system enables growers to respond quickly and enhances large-scale smart crop health monitoring. |
| format | Article |
| id | doaj-art-dd3b7c97b1e141b1a8fbdf89f3205aea |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-dd3b7c97b1e141b1a8fbdf89f3205aea2025-08-20T02:33:42ZengMDPI AGComputers2073-431X2025-05-0114517110.3390/computers14050171ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial IntelligenceAbdullah Al Noman0Amira Hossain1Anamul Sakib2Jesika Debnath3Hasib Fardin4Abdullah Al Sakib5Rezaul Haque6Md. Redwan Ahmed7Ahmed Wasif Reza8M. Ali Akber Dewan9Department of Information Technology, Westcliff University, Irvine, CA 92614, USADepartment of Computer Science, Westcliff University, Irvine, CA 92614, USADepartment of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USADepartment of Computer Science, Westcliff University, Irvine, CA 92614, USADepartment of Engineering Management, Westcliff University, Irvine, CA 92614, USADepartment of Information Technology, Westcliff University, Irvine, CA 92614, USADepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshSchool of Computing and Information Systems, Athabasca University, Athabasca, AB T9S 3A3, CanadaMango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretability. To address these challenges, this study introduces the ViX-MangoEFormer, which combines convolutional kernels and self-attention to effectively diagnose multiple mango leaf conditions in both balanced and imbalanced image sets. To benchmark against ViX-MangoEFormer, we developed a stacking ensemble model (MangoNet-Stack) that utilizes five transfer learning networks as base learners. All models were trained with Grad-CAM produced pixel-level explanations. In a combined dataset of 25,530 images, ViX-MangoEFormer achieved an F1 score of 99.78% and a Matthews Correlation Coefficient (MCC) of 99.34%. This performance consistently outperformed individual pre-trained models and MangoNet-Stack. Additionally, data augmentation has improved the performance of every architecture compared to its non-augmented version. Cross-domain tests on morphologically similar crop leaves confirmed strong generalization. Our findings validate the effectiveness of transformer attention and XAI in mango leaf disease detection. ViX-MangoEFormer is deployed as a web application that delivers real-time predictions, probability scores, and visual rationales. The system enables growers to respond quickly and enhances large-scale smart crop health monitoring.https://www.mdpi.com/2073-431X/14/5/171Vision Transformer (ViT)explainable AI (XAI)ensemble learningprecision agriculturemango leaf classification |
| spellingShingle | Abdullah Al Noman Amira Hossain Anamul Sakib Jesika Debnath Hasib Fardin Abdullah Al Sakib Rezaul Haque Md. Redwan Ahmed Ahmed Wasif Reza M. Ali Akber Dewan ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence Computers Vision Transformer (ViT) explainable AI (XAI) ensemble learning precision agriculture mango leaf classification |
| title | ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence |
| title_full | ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence |
| title_fullStr | ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence |
| title_full_unstemmed | ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence |
| title_short | ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence |
| title_sort | vix mangoeformer an enhanced vision transformer efficientformer and stacking ensemble approach for mango leaf disease recognition with explainable artificial intelligence |
| topic | Vision Transformer (ViT) explainable AI (XAI) ensemble learning precision agriculture mango leaf classification |
| url | https://www.mdpi.com/2073-431X/14/5/171 |
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