MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification
Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Man...
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IEEE
2025-01-01
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| author | Md. Eshmam Rayed Jamin Rahman Jim Md Juniadul Islam M. F. Mridha Md Mohsin Kabir Md. Jakir Hossen |
| author_facet | Md. Eshmam Rayed Jamin Rahman Jim Md Juniadul Islam M. F. Mridha Md Mohsin Kabir Md. Jakir Hossen |
| author_sort | Md. Eshmam Rayed |
| collection | DOAJ |
| description | Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture applications, contributing to enhanced crop health monitoring and sustainable mango cultivation practices. |
| format | Article |
| id | doaj-art-90c7a74583754c0bbce56a3fb6fbcc72 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-90c7a74583754c0bbce56a3fb6fbcc722025-08-20T02:03:08ZengIEEEIEEE Access2169-35362025-01-0113939779400810.1109/ACCESS.2025.357145011006991MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease ClassificationMd. Eshmam Rayed0https://orcid.org/0009-0008-2118-5565Jamin Rahman Jim1https://orcid.org/0000-0003-2854-5144Md Juniadul Islam2https://orcid.org/0009-0003-7248-0349M. F. Mridha3https://orcid.org/0000-0001-5738-1631Md Mohsin Kabir4https://orcid.org/0000-0001-9624-5499Md. Jakir Hossen5https://orcid.org/0000-0002-9978-7987Department of Computer Science and Engineering, American International University–Bangladesh, Dhaka, BangladeshSuperior Polytechnic School, Universitat de Girona, Girona, SpainDepartment of Computer Science and Engineering, American International University–Bangladesh, Dhaka, BangladeshDepartment of Computer Science and Engineering, American International University–Bangladesh, Dhaka, BangladeshDivision of Computer Science and Software Engineering, Mälardalens University, Västerås, SwedenCenter for Advanced Analytics (CAA), COE for Artificial Intelligence Faculty of Engineering and Technology (FET), Multimedia University, Melaka, MalaysiaAddressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture applications, contributing to enhanced crop health monitoring and sustainable mango cultivation practices.https://ieeexplore.ieee.org/document/11006991/Image processingconvolutional neural networkdeep learningmango leaf diseaseimage classificationexplainable AI |
| spellingShingle | Md. Eshmam Rayed Jamin Rahman Jim Md Juniadul Islam M. F. Mridha Md Mohsin Kabir Md. Jakir Hossen MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification IEEE Access Image processing convolutional neural network deep learning mango leaf disease image classification explainable AI |
| title | MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification |
| title_full | MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification |
| title_fullStr | MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification |
| title_full_unstemmed | MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification |
| title_short | MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification |
| title_sort | mangoleafxnet an explainable deep learning model for accurate mango leaf disease classification |
| topic | Image processing convolutional neural network deep learning mango leaf disease image classification explainable AI |
| url | https://ieeexplore.ieee.org/document/11006991/ |
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