Lightweight transformer based WT-MobileViT framework for plant disease detection

Abstract The rapid spread of plant diseases significantly threatens global food security. Early and accurate detection of these diseases is crucial for effective disease management and crop protection. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. In recent years, computer vision-based machine learning techniques have been proposed by researchers to overcome the challenge. In this paper, we have proposed a WT-MobileViT, a lightweight hybrid model that integrates frequency domain information with the MobileViT framework for the classification of plant diseases. The proposed method first performs a one-level Haar Discrete Wavelet Transform (DWT) to decompose the input image into low-frequency and high-frequency sub-bands, preserving complementary structural and texture information, and concatenates with the original RGB image to construct a multi-frequency feature representation. In this article, we have created a tea leaf disease dataset with RGB images in field conditions. The proposed model was experimented on four datasets and achieved an accuracy rate of 99.44% in tea leaf images, 99.68% in the rice dataset, 99.17% in the bean dataset, and 99.21% in plantvillage dataset. The experimental results show that the proposed model outperforms several state-of-the-art deep learning models with fewer parameters.