Tomato Leaf Disease Detection Using Convolution Neural Network
Hareem Kibriya, Rimsha Rafique, Wakeel Ahmad, Syed Muhammad Adnan
Abstract
The quality and quantity of the crop are significantly affected by numerous diseases in plants. In this regard, an early detection of such diseases is highly effective. Tomato is one of the important crops that is produced in large quantities with high commercial value. Several types of tomato diseases affect the crop at an alarming rate. In this paper, we deployed two Convolution Neural Network (CNN) based models i.e. GoogLeNet and VGG16 for tomato leaf disease classification. The proposed work aims to find the best solution to the problem of tomato leaf disease detection using a deep learning approach. VGG16 obtained 98% accuracy while GoogLeNet obtained 99.23% on Plant Village dataset containing 10735 leaf images. The proposed system can be used in tomato fields for early detection of disease to avoid production loss.