Plant leaf disease detection using vision transformers for precision agriculture.

Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs) have revealed promise in leaf disease detection and classification. This research proposes PLA-ViT, or Precision Leaf Analysis with Vision Transformers, to improve agricultural monitoring. Vision Transformers (ViTs) outperform other neural networks because they employ self-attention to find global contextual information. The approach uses data augmentation, normalization, and bilateral filtering to increase generalization and image quality. Transfer learning using pre-trained ViTs reduces computing load and improves feature extraction. The model may be adjusted by hyperparameter tuning and adaptive learning rate scheduling for robust performance with minimal overfitting. In experiments, PLA-ViT outperforms other neural network-based models regarding detection accuracy, disease localization performance, inference time, and computational complexity. By attaching the system to IoT sensors, stakeholders may observe farms in real time and take timely measures like pesticide treatment or plant isolation. This novel method shows that transformer-based designs might help progress in precision agriculture.