Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability
Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a public...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14847-7 |
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| author | Sankar Murugesan Jayaprakash Chinnadurai Saravanan Srinivasan Sandeep Kumar Mathivanan Radha Raman Chandan Usha Moorthy |
| author_facet | Sankar Murugesan Jayaprakash Chinnadurai Saravanan Srinivasan Sandeep Kumar Mathivanan Radha Raman Chandan Usha Moorthy |
| author_sort | Sankar Murugesan |
| collection | DOAJ |
| description | Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants. This dataset was uniformly split into training, validation, and test sets to obtain consistent and unbiased model evaluations. The data pre-processing also involved pre-processing steps suitable for DL architectures to keep the input the same among all the models.We use several state-of-the-art pre-trained ConvNets models for the baselines, such as EfficientNetV2, ConvNeXt, Swin Transformer, and Vi-Transformer (ViT), to have an outlook on the performance. A new ConvNet-ViT hybrid model combines the ConvNet and ViT layers for local feature extraction and maintaining the global context. The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. Its outstanding performance of the state-of-the-art pre-trained top models positions itself as a solid model for practical agricultural use. Fusing the ConvNet and transformer frameworks jointly is beneficial for improving classification performance in image-based disease detection work. |
| format | Article |
| id | doaj-art-1edb985f06464c8d9fbe749c249af3d0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1edb985f06464c8d9fbe749c249af3d02025-08-20T03:42:38ZengNature PortfolioScientific Reports2045-23222025-08-0115112210.1038/s41598-025-14847-7Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretabilitySankar Murugesan0Jayaprakash Chinnadurai1Saravanan Srinivasan2Sandeep Kumar Mathivanan3Radha Raman Chandan4Usha Moorthy5Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologySchool of Computer Science and Engineering, Galgotias UniversityDepartment of Computer Science and Engineering, SRM Institute of Science and TechnologySchool of Computer Science and Engineering, Galgotias UniversityDepartment of Computer Science, School of Management Sciences (SMS)School of Computer Engineering, Manipal Institute of Technology, Bengaluru,, Manipal Academy of Higher EducationAbstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants. This dataset was uniformly split into training, validation, and test sets to obtain consistent and unbiased model evaluations. The data pre-processing also involved pre-processing steps suitable for DL architectures to keep the input the same among all the models.We use several state-of-the-art pre-trained ConvNets models for the baselines, such as EfficientNetV2, ConvNeXt, Swin Transformer, and Vi-Transformer (ViT), to have an outlook on the performance. A new ConvNet-ViT hybrid model combines the ConvNet and ViT layers for local feature extraction and maintaining the global context. The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. Its outstanding performance of the state-of-the-art pre-trained top models positions itself as a solid model for practical agricultural use. Fusing the ConvNet and transformer frameworks jointly is beneficial for improving classification performance in image-based disease detection work.https://doi.org/10.1038/s41598-025-14847-7Plant leaf diseaseClassificationConvNetDeep learningVision transformerHybrid ConvNet-ViT |
| spellingShingle | Sankar Murugesan Jayaprakash Chinnadurai Saravanan Srinivasan Sandeep Kumar Mathivanan Radha Raman Chandan Usha Moorthy Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability Scientific Reports Plant leaf disease Classification ConvNet Deep learning Vision transformer Hybrid ConvNet-ViT |
| title | Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability |
| title_full | Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability |
| title_fullStr | Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability |
| title_full_unstemmed | Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability |
| title_short | Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability |
| title_sort | robust multiclass classification of crop leaf diseases using hybrid deep learning and grad cam interpretability |
| topic | Plant leaf disease Classification ConvNet Deep learning Vision transformer Hybrid ConvNet-ViT |
| url | https://doi.org/10.1038/s41598-025-14847-7 |
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