Disease Detection in Tomato Plants Using CNN

Tomato is one of the highest yield cash crops in the World and is produced in large quantity to fulfil the huge market demand. In India tomato season prevails throughout the year and has high commercial value, it is harvested four times a year based on the geo location. Diseases indirectly affect the quality and yield of the crops. In this regard always prevention is better than cure is the best way to go, so early diseases detection plays a crucial role for high quality yielding and plays a crucial role in scientific agricultural practice with proper pesticides for specific category of disease. The objective of the study is to categorise tomato diseases using deep hidden layers of Convolution Neural Network (CNN),for the identification and classifying 8different classes of diseases in tomato leaf with an accuracy around 94.17%, our model which uses CNN allows the farmers to get an accurate disease prediction based on the photo uploaded by them and remedies for the diseases is suggested, the processing of image is done by using feature extraction methods where feature such as shape, colour, and texture are taken in consideration.