Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers
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Date
2025-08-06
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers in Artificial Intelligence
Abstract
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.
Sustainable Development Goals
SDG - 2 : Zero Hunger
SDG - 3 : Good Health and Well-being
SDG - 9 : Industry, Innovation, and Infrastructure
SDG -12 : Responsible Consumption and Production
SDG -13 : Climate Action
Keywords
bean rust, bean anthracnose, deep learning, Vision Transformers (ViT), adversarial attacks, Fast Gradient Sign Method