A CNN Method Based Predictive Model for Tomato Leaf Disease Prediction
Jyoti Agarwal, Shelly Gupta, Neha Sharma, Mahesh Manchanda
Abstract
Plant diseases are emerging problem in agriculture sector which occurs due to various bacteria, viruses etc. There is lack of awareness among the farmers about these kinds of diseases which has adverse effect on crop production rate. One of the very important crops that plays significant role in Indian economy is tomato. In India, production of tomato crop is vast which has direct impact on Indian economy. Various kind of diseases can be cultivated in tomato plants also which can be harmful for the crop and farmers face economical loses. Due to these issues, it is important to detect tomato leaf diseases to prevent the crop as well as economic damages. The purpose of this study is to suggest an easy and precise method to identify and categorize diseases of tomato leaves. For this reason, a CNN method is applied as they employ automatic feature extraction as well as classification of the input image into different classes of diseases. Experiment is done on an online dataset in which images are classified into 10 different types of diseases. Proposed model was able to receive 93% of accurate results for detecting the correct diseases in tomato leaves which shows that proposed CNN model can be used as a feasible and efficient technique for identifying tomato leaf diseases in diverse circumstances.