Plant leaf disease detection using vision transformers for precision agriculture

Abstract

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.

Plant phenotyping relevance

葉画像から植物病害を検出・分類・局在化するVision Transformer手法の開発と性能比較が研究の中心であり、植物の病害状態を直接推定している。

Code and data availability

The article describes a PLA-ViT leaf disease detection model but contains no public dataset deposit, author code release, trained model checkpoint, or supplement with phenotyping data. The Plant Village dataset is only mentioned in the literature survey as prior work, and no availability or URL language for paper-pheny

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