Identification and Classification of Tomato Leaf Diseases Using Machine Learning Techniques

Abstract

Tomato is cultivated in all countries of the world in fields, glasshouses etc. China, India, USA, Turkey, Egypt, Iran, Italy, Spain and Brazil are the important countries which are cultivating tomatoes. It is most commonly and widely cultivated in India. India is one of the countries in harvesting tomatoes. Tomato is a vital vegetable yield with respect to both income and food. Tomatoes are for the most part summer crops, yet it tends to improve steadily. Naturally, it contains A and C of vitamins which also acts as an antioxidant to prevent cancerous cells. Since the organic product contains novel features, the demand remains the same. A significant and unique feature with high nutrients gains the importance in tomatoes cultivation. Challenges towards the cultivation of tomato made us to plan for an automated machine to detect infection and to increase the productivity. This system automatically detects the infected parts and classify the types of disease which occur on the leaf like early blight, bacterial wilt, Leaf Spot, tomato mosaic virus, septoria leaf spot, leaf curl virus, and tomato spotted wilt disease using gradient anisotropic diffusion filter for pre-processing and then features are extracted using GLCM from the pre-processed leaf