ViT-SmartAgri: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture

Abstract

Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed ViT-SmartAgri is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area.

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

“…Despite its deployment in a smartphone application, this research lacked certain features compared to our research. Additionally, there was no mention of a centralized reporting repository, and the trained model might lack precision in distinguishing leaves with complex backgrounds [30].…”

An efficient deep learning model for tomato disease detection

“…This has caused serious food safety issues and significantly reduced the economic benefits of tomato cultivation. Consequently, rapid and accurate disease detection plays a crucial role in the prevention and control of tomato diseases [ 8 , 9 ]. Currently, the identification and control of tomato diseases primarily rely on empirical methods (Fig.…”