Classification and Detection of Tomato Leaf Disease

Abstract

The tomato crop is a significant staple with a high commercial value on the Indian market and is produced in enormous quantities. Diseases are harmful to the health of the plant, which has an impact on its growth. It is essential to monitor the progress of the farmed crop to ensure minimal losses. Many different tomato diseases attack the crop's leaves at frightening rates. The primary goal of the proposed effort is to identify a simple method for detecting tomato leaf disease while employing limited computational resources to produce outcomes that are comparable to state-of- the-art. Automatic feature extraction is used by neural network models to help categorize the input image into the appropriate illness classifications. The average accuracy of this suggested system is between 94 to 95 percent, demonstrating the viability of the neural network approach even in challenging circumstances. Keywords: Agriculture, Convolution Neural Network, machine learning, Support Vector Machine.