Smart plant disease diagnosis using multiple deep learning and web application integration
Accurate and efficient plant disease diagnosis is crucial for sustainable agriculture and global food security, as diseases significantly impact crop productivity. Despite advancements in deep learning, the performance and scalability of many models remain limited. This study addresses this gap by e...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154325003199 |
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| author | Ahmed M.S. Kheir Anis Koubaa Vinothkumar Kolluru Sudeep Mungara Til Feike |
| author_facet | Ahmed M.S. Kheir Anis Koubaa Vinothkumar Kolluru Sudeep Mungara Til Feike |
| author_sort | Ahmed M.S. Kheir |
| collection | DOAJ |
| description | Accurate and efficient plant disease diagnosis is crucial for sustainable agriculture and global food security, as diseases significantly impact crop productivity. Despite advancements in deep learning, the performance and scalability of many models remain limited. This study addresses this gap by evaluating MobileViTv2, EfficientNet-B7, and a hybrid MobileViTv2-EfficientNet-B7 approach for classifying plant leaf images into four categories: healthy, rust, scab, and multiple diseases. Using a publicly available dataset of annotated leaf images, the models were trained and tested under optimized conditions. MobileViTv2 emerged as the superior model, achieving the highest classification accuracy (94 %) and F1 score (0.94). It demonstrated exceptional generalization capabilities, with Receiver Operating Characteristic (ROC) Area Under Curve (AUC) values of 0.95 for healthy, 0.97 for rust, and 0.99 for scab. In contrast, EfficientNet-B7 and the hybrid model performed moderately, highlighting MobileViTv2's efficiency in handling diverse image features. To demonstrate real-world applicability, the MobileViTv2 model was deployed in a web-based application. This platform enables real-time plant disease diagnosis with high confidence, identifying conditions such as rust (85.3 % confidence) and healthy leaves (90.2 % confidence). The user-friendly interface facilitates its integration into precision agriculture. This study highlights the strengths of MobileViTv2 for disease diagnosis, its scalability, and its potential to support decision-making in agriculture. Future work will focus on expanding the model to other crops and incorporating environmental variables for enhanced disease prediction. This research bridges the gap between advanced AI models and practical agricultural applications, offering a robust solution for early disease detection. |
| format | Article |
| id | doaj-art-ec8b56d24e094cb585e0149ba605b19a |
| institution | DOAJ |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-ec8b56d24e094cb585e0149ba605b19a2025-08-20T03:10:25ZengElsevierJournal of Agriculture and Food Research2666-15432025-06-012110194810.1016/j.jafr.2025.101948Smart plant disease diagnosis using multiple deep learning and web application integrationAhmed M.S. Kheir0Anis Koubaa1Vinothkumar Kolluru2Sudeep Mungara3Til Feike4Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, 14532 Kleinmachnow, Germany; Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, 12112, Egypt; Corresponding author. Institute for Strategies and Technology Assessment, Julius Kühn Institute (JKI), Federal Research Centre for Cultivated Plants, 14532, Kleinmachnow, Germany.Alfaisal University, Saudi Arabia, RiyadhDepartment of Data Science, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USAStevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USAJulius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, 14532 Kleinmachnow, GermanyAccurate and efficient plant disease diagnosis is crucial for sustainable agriculture and global food security, as diseases significantly impact crop productivity. Despite advancements in deep learning, the performance and scalability of many models remain limited. This study addresses this gap by evaluating MobileViTv2, EfficientNet-B7, and a hybrid MobileViTv2-EfficientNet-B7 approach for classifying plant leaf images into four categories: healthy, rust, scab, and multiple diseases. Using a publicly available dataset of annotated leaf images, the models were trained and tested under optimized conditions. MobileViTv2 emerged as the superior model, achieving the highest classification accuracy (94 %) and F1 score (0.94). It demonstrated exceptional generalization capabilities, with Receiver Operating Characteristic (ROC) Area Under Curve (AUC) values of 0.95 for healthy, 0.97 for rust, and 0.99 for scab. In contrast, EfficientNet-B7 and the hybrid model performed moderately, highlighting MobileViTv2's efficiency in handling diverse image features. To demonstrate real-world applicability, the MobileViTv2 model was deployed in a web-based application. This platform enables real-time plant disease diagnosis with high confidence, identifying conditions such as rust (85.3 % confidence) and healthy leaves (90.2 % confidence). The user-friendly interface facilitates its integration into precision agriculture. This study highlights the strengths of MobileViTv2 for disease diagnosis, its scalability, and its potential to support decision-making in agriculture. Future work will focus on expanding the model to other crops and incorporating environmental variables for enhanced disease prediction. This research bridges the gap between advanced AI models and practical agricultural applications, offering a robust solution for early disease detection.http://www.sciencedirect.com/science/article/pii/S2666154325003199Plant disease diagnosisMobileViTv2EfficientNet-B7Hybrid deep learning modelsPrecision agricultureWeb-based application |
| spellingShingle | Ahmed M.S. Kheir Anis Koubaa Vinothkumar Kolluru Sudeep Mungara Til Feike Smart plant disease diagnosis using multiple deep learning and web application integration Journal of Agriculture and Food Research Plant disease diagnosis MobileViTv2 EfficientNet-B7 Hybrid deep learning models Precision agriculture Web-based application |
| title | Smart plant disease diagnosis using multiple deep learning and web application integration |
| title_full | Smart plant disease diagnosis using multiple deep learning and web application integration |
| title_fullStr | Smart plant disease diagnosis using multiple deep learning and web application integration |
| title_full_unstemmed | Smart plant disease diagnosis using multiple deep learning and web application integration |
| title_short | Smart plant disease diagnosis using multiple deep learning and web application integration |
| title_sort | smart plant disease diagnosis using multiple deep learning and web application integration |
| topic | Plant disease diagnosis MobileViTv2 EfficientNet-B7 Hybrid deep learning models Precision agriculture Web-based application |
| url | http://www.sciencedirect.com/science/article/pii/S2666154325003199 |
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