Potato Leaf Disease Detection Using VIT

Abstract

Potatoes are important for global meals security, but are liable to diseases including fungi, nematodes, viruses, early Blight,and bug damage, which reduces yields and financial losses for farmers. The mission takes benefit of the imaginative and prescient transformer (VIT) to detect accurate and efficient potato disease by using collecting a diverse dataset of potato leaf pics and implementing preprocess and statistics expansion strategies for strong version education. A safe login, an administrator dashboard, and a consumer with a chatbot for real time conversation-will increase growth, whilst the integrated
climate forecast will help farmers to estimate the outbreak of the sickness. Better photograph category abilities of VIT models will be evaluated with non-stop improvement to growth performance, accuracy, recollect and F1-rating. With practical treatment and practical gadget consisting of climate forecasts,this system offers rights to support global food safety, reduce pesticides and promote productivity.