Plant Disease Detection on Multispectral Images using Vision Transformers

This study tackles the challenge of early disease detection in outdoor plants for effective agricultural management. Conventional methods face hurdles such as time-intensive procedures and specialized expertise demands. The solution lies in cutting-edge deep learning models, specifically hybrid vision transformers (ViTs), synergizing with convolutional neural networks (CNNs) for heightened precision. By evaluating a comprehensive multispectral dataset captured in real-world conditions, the hybrid ViT approach achieved an impressive accuracy of 88.86%. This research underscores the potential of ViTs in precisely identifying plant diseases even before they become visible, particularly through the use of multispectral imagery. This breakthrough promises to revolutionize agricultural practices with advanced disease management techniques.