Year: 2025
Type: article
Abstract: Plant diseases pose a serious threat to global food security and sustainable agriculture, causing significant economic losses and affecting rural livelihoods. Traditional disease detection methods, such as manual inspection and lab tests, often lack efficiency, scalability, and accuracy. This paper explores the use of vision transformers (ViTs), a cutting-edge deep learning technique, to enhance plant disease detection. ViTs utilize self-attention mechanisms to analyze complex patterns in plant images, enabling precise and automated classification. A thorough review of deep learning applications in agriculture highlights the growing interest in ViTs for disease identification. This study also details an effective methodology for training and evaluating ViT models on a well-balanced dataset of 55 plant disease classes. Experimental findings confirm the superior accuracy of ViTs, showcasing their transformative potential in precision agriculture. By improving early disease diagnosis, ViTs can contribute to more sustainable farming practices and increased agricultural productivity.
Author: Prof. Minakshi Ramteke
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409378190