Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves
Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious...
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Computer Vision Center Press
2025-05-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
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| Online Access: | https://elcvia.cvc.uab.cat/article/view/2009 |
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| author | Menaka Radhakrishnan |
| author_facet | Menaka Radhakrishnan |
| author_sort | Menaka Radhakrishnan |
| collection | DOAJ |
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Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious and often inaccurate. Hence the use of machine learning and convolutional neural networks have proven to be of great advantage in terms of accuracy, reliability, ease of implementation etc. This paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss. Further the models have been combined using feature fusion, and their accuracies compared. Finally, a combination of ResNet and AlexNet has been proposed with an impressive accuracy of 99.97%. Further, Grad-CAM (Gradient-weighted Class Activation Mapping) has been implemented to highlight important regions in the leaf images which improves visualization. This can potentially provide an accurate identification and classification of plant diseases based on leaf images.
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| format | Article |
| id | doaj-art-b40ceddecf6a4b5986ce994779b8498d |
| institution | OA Journals |
| issn | 1577-5097 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj-art-b40ceddecf6a4b5986ce994779b8498d2025-08-20T02:25:44ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972025-05-0124110.5565/rev/elcvia.2009Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves Menaka Radhakrishnan0Vellore Institute of Technology, Chennai Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious and often inaccurate. Hence the use of machine learning and convolutional neural networks have proven to be of great advantage in terms of accuracy, reliability, ease of implementation etc. This paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss. Further the models have been combined using feature fusion, and their accuracies compared. Finally, a combination of ResNet and AlexNet has been proposed with an impressive accuracy of 99.97%. Further, Grad-CAM (Gradient-weighted Class Activation Mapping) has been implemented to highlight important regions in the leaf images which improves visualization. This can potentially provide an accurate identification and classification of plant diseases based on leaf images. https://elcvia.cvc.uab.cat/article/view/2009plant disease, deep learning, CNN, Explainable AI, Grad-CAM, mango leaves, AlexNet, ResNet, model fusion |
| spellingShingle | Menaka Radhakrishnan Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves ELCVIA Electronic Letters on Computer Vision and Image Analysis plant disease, deep learning, CNN, Explainable AI, Grad-CAM, mango leaves, AlexNet, ResNet, model fusion |
| title | Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves |
| title_full | Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves |
| title_fullStr | Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves |
| title_full_unstemmed | Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves |
| title_short | Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves |
| title_sort | implementation of explainable ai in deep learning methods for multiclass classification of plant diseases in mango leaves |
| topic | plant disease, deep learning, CNN, Explainable AI, Grad-CAM, mango leaves, AlexNet, ResNet, model fusion |
| url | https://elcvia.cvc.uab.cat/article/view/2009 |
| work_keys_str_mv | AT menakaradhakrishnan implementationofexplainableaiindeeplearningmethodsformulticlassclassificationofplantdiseasesinmangoleaves |