Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers
Common bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robust...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1643582/full |
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| author | Upendo Mwaibale Neema Mduma Hudson Laizer Bonny Mgawe |
| author_facet | Upendo Mwaibale Neema Mduma Hudson Laizer Bonny Mgawe |
| author_sort | Upendo Mwaibale |
| collection | DOAJ |
| description | Common bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robustness under real-world farm conditions. A dataset of 100,000 annotated images augmented with geometric, color, and FGSM-based perturbations, simulating field variability. FGSM was selected for its computational efficiency in low-resource settings. The model, fine-tuned using transfer learning and validated through cross-validation, achieved an accuracy of 99.4%. Results highlight the effectiveness of integrating adversarial robustness to enhance model reliability for mobile-based plant disease detection in resource-constrained environments. |
| format | Article |
| id | doaj-art-b9f2ff06973e4e5cb4032a4187b01582 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-b9f2ff06973e4e5cb4032a4187b015822025-08-20T04:02:13ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.16435821643582Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision TransformersUpendo Mwaibale0Neema Mduma1Hudson Laizer2Bonny Mgawe3Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, TanzaniaComputational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, TanzaniaLife Sciences and Bio-engineering (LiSBE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, TanzaniaComputational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, TanzaniaCommon bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robustness under real-world farm conditions. A dataset of 100,000 annotated images augmented with geometric, color, and FGSM-based perturbations, simulating field variability. FGSM was selected for its computational efficiency in low-resource settings. The model, fine-tuned using transfer learning and validated through cross-validation, achieved an accuracy of 99.4%. Results highlight the effectiveness of integrating adversarial robustness to enhance model reliability for mobile-based plant disease detection in resource-constrained environments.https://www.frontiersin.org/articles/10.3389/frai.2025.1643582/fullbean rustbean anthracnosedeep learningVision Transformers (ViT)adversarial attacksFast Gradient Sign Method |
| spellingShingle | Upendo Mwaibale Neema Mduma Hudson Laizer Bonny Mgawe Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers Frontiers in Artificial Intelligence bean rust bean anthracnose deep learning Vision Transformers (ViT) adversarial attacks Fast Gradient Sign Method |
| title | Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers |
| title_full | Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers |
| title_fullStr | Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers |
| title_full_unstemmed | Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers |
| title_short | Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers |
| title_sort | enhancing detection of common bean diseases using fast gradient sign method trained vision transformers |
| topic | bean rust bean anthracnose deep learning Vision Transformers (ViT) adversarial attacks Fast Gradient Sign Method |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1643582/full |
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