Employing Visiοn Transfοrmеrs for High-Prеcisiοn Sugarcanе Disеasе Classificatiοn: A Dееp Lеarning Pеrspеctiνе

This research is realizing my long-term dream, dedicated to my following farmers. By accurately identifying plant diseases, ‎the main goal of this study is to assist farmers in increasing their agricultural yield. To do this, we collected a dataset of 2,521 images, which we categorized into five distinct types of plant diseases: Cеrcÿspÿra Lеaf Spÿt (462 images), Hеlminthοspοrium ‎Lеaf Disеase (522 images), Rust (514 images), Red Dοt (518 images), and Yellow Leaf Disease (50 images). We used Visiÿn ‎Transforms (Vits) as a novel approach to plant disease detection in this investigation. By leveraging the power of VITs, this research ‎seeks to improve the precision and effectiveness of disease diagnosis, providing farmers with an advanced technical tool for early ‎disease diagnosis. The experimental results showed that Vits were effective in differentiating between a variety of plant diseases, ‎with an overall classification accuracy of 96.45%. This work is ultimately intended to provide farmers with AI-driven solutions that ‎improve agricultural productivity and sustainability. The results of this study contribute to the advancement of precision in agriculture, assisting farmers in making informed decisions and reducing costs through timely intervention‎.