A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer
Abstract Recently, scientists have widely utilized Artificial Intelligence (AI) approaches in intelligent agriculture to increase the productivity of the agriculture sector and overcome a wide range of problems. Detection and classification of plant diseases is a challenging problem due to the vast...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01764-x |
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| author | Sherihan Aboelenin Foriaa Ahmed Elbasheer Mohamed Meselhy Eltoukhy Walaa M. El-Hady Khalid M. Hosny |
| author_facet | Sherihan Aboelenin Foriaa Ahmed Elbasheer Mohamed Meselhy Eltoukhy Walaa M. El-Hady Khalid M. Hosny |
| author_sort | Sherihan Aboelenin |
| collection | DOAJ |
| description | Abstract Recently, scientists have widely utilized Artificial Intelligence (AI) approaches in intelligent agriculture to increase the productivity of the agriculture sector and overcome a wide range of problems. Detection and classification of plant diseases is a challenging problem due to the vast numbers of plants worldwide and the numerous diseases that negatively affect the production of different crops. Early detection and accurate classification of plant diseases is the goal of any AI-based system. This paper proposes a hybrid framework to improve classification accuracy for plant leaf diseases significantly. This proposed model leverages the strength of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), where an ensemble model, which consists of the well-known CNN architectures VGG16, Inception-V3, and DenseNet20, is used to extract robust global features. Then, a ViT model is used to extract local features to detect plant diseases precisely. The performance proposed model is evaluated using two publicly available datasets (Apple and Corn). Each dataset consists of four classes. The proposed hybrid model successfully detects and classifies multi-class plant leaf diseases and outperforms similar recently published methods, where the proposed hybrid model achieved an accuracy rate of 99.24% and 98% for the apple and corn datasets. |
| format | Article |
| id | doaj-art-76a9be64063c4fa2aa186b6386acd8f4 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-76a9be64063c4fa2aa186b6386acd8f42025-02-09T13:01:25ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111211710.1007/s40747-024-01764-xA hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformerSherihan Aboelenin0Foriaa Ahmed Elbasheer1Mohamed Meselhy Eltoukhy2Walaa M. El-Hady3Khalid M. Hosny4Department of Information Technology, College of Computing and Information Technology at Khulais, University of JeddahDepartment of Information Systems, College of Computing and Information Technology at Khulais, University of JeddahDepartment of Information Technology, College of Computing and Information Technology at Khulais, University of JeddahDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig UniversityDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig UniversityAbstract Recently, scientists have widely utilized Artificial Intelligence (AI) approaches in intelligent agriculture to increase the productivity of the agriculture sector and overcome a wide range of problems. Detection and classification of plant diseases is a challenging problem due to the vast numbers of plants worldwide and the numerous diseases that negatively affect the production of different crops. Early detection and accurate classification of plant diseases is the goal of any AI-based system. This paper proposes a hybrid framework to improve classification accuracy for plant leaf diseases significantly. This proposed model leverages the strength of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), where an ensemble model, which consists of the well-known CNN architectures VGG16, Inception-V3, and DenseNet20, is used to extract robust global features. Then, a ViT model is used to extract local features to detect plant diseases precisely. The performance proposed model is evaluated using two publicly available datasets (Apple and Corn). Each dataset consists of four classes. The proposed hybrid model successfully detects and classifies multi-class plant leaf diseases and outperforms similar recently published methods, where the proposed hybrid model achieved an accuracy rate of 99.24% and 98% for the apple and corn datasets.https://doi.org/10.1007/s40747-024-01764-xFarmingPlant leaf disease classificationHybrid modelDeep learningConvolutional neural networks (CNNs)Feature concatenation |
| spellingShingle | Sherihan Aboelenin Foriaa Ahmed Elbasheer Mohamed Meselhy Eltoukhy Walaa M. El-Hady Khalid M. Hosny A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer Complex & Intelligent Systems Farming Plant leaf disease classification Hybrid model Deep learning Convolutional neural networks (CNNs) Feature concatenation |
| title | A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| title_full | A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| title_fullStr | A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| title_full_unstemmed | A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| title_short | A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| title_sort | hybrid framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer |
| topic | Farming Plant leaf disease classification Hybrid model Deep learning Convolutional neural networks (CNNs) Feature concatenation |
| url | https://doi.org/10.1007/s40747-024-01764-x |
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