Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches

Abstract

Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.

Plant phenotyping relevance

植物病害という植物状態をマルチスペクトル画像とCNN/ViTで推定する手法が研究の中心であり、データ収集、波長比較、モデル性能評価を含むため収録対象。

Code and data availability

The paper's Data Availability Statement and conclusions provide public Google Drive links to the authors' balanced and unbalanced multispectral plant disease image datasets used in this study. No code or model checkpoints are shared.