Tomato leaves Diseases Classification using Deep Learning Architecture

Abstract

Disease in the plants can significantly reduce the quality and quantity of the plant yield. A disease is quite natural in plants, which is why disease detection in plants is so important in farming. Diseases are responsible for both direct and indirect mon etary loss to the farmer. Classification of plant disease is imperative, which helps in enchanting a proper management step towards preventing the further spread of the disease once the disease is correctly classified and treated suitably. Deep learning methods have demonstrated significant improvements in plant leaf classification performance. The rapid and precise diagnosis of disease severity will aid in reducing yield losses. The work presented here focuses on the classification of Tomato plant leaves disease. The nine classes of healthy and diseases of tomato plant leaves from the PlantVillage dataset that occurs in Indian states are studied here. The diseases of tomato plant leaf analyzed here are Bacterial Spot (BS), Early Blight (EB), Late Blight (LB), Leaf Mold (LM), Mosaic Virus (MV), Septoria Leaf Spot (SLS), Target Spot (TS), and Yellow Leaf Curl Virus (YLCV).