TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING

In India's agricultural lands, tomatoes are the most widely planted vegetable crop. In spite it can grow in warmer climates, certain climatic conditions and other factors may contribute to the growth of the tomato plant. In addition to these natural and man-made disasters, crop disease is a serious problem in agricultural production leading to economic losses. Therefore, early detection of disease can provide better results than current diagnostic algorithms. As a consequence, in-depth computer-based learning methods may be used to diagnose diseases early. This study carefully examines the disease classification and diagnostic techniques used to diagnose tomato leaf disease. The advantages and disadvantages of the methods provided are also discussed in this study. Eventually, using hybrid deep- learning architecture, this study provides a way to diagnose diseases early to diagnose tomato leaf disease.