Automatic detection of tomato leaf contamination portion using deep neural network.

India yields many varieties of crops. Among them tomato crop is the vital staple in Indian market. Many farmers produce tomato crop cross India. Many types of diseases will affect the crop due to many reasons. So, it is important to prevent the crop from being affected by those diseases and also curing the affected portion with suitable techniques. Our model is to predict the tomato leaf's health and also identify type of the disease it is affected with. The proposed model targets to reduce the amount of chemical fertilized used without having proper knowledge of the disease affected. Also help the cultivator to grow the tomato plants properly. This model is developed using Deep Convolutional Neural Network (CNN) model called TOMLeNet and Adam optimizer is used to predict the disease in the early stage and to avoid loss to the farmer. Neural network models make use of automatic feature extraction for the classification of the input image into their respective disease classes. The proposed model uses the tomato leaf disease data set publicly available in Kaggle and model generated an average accuracy of 97.42% using the features provide and even in unfavorable conditions. [ABSTRACT FROM AUTHOR]