Tomato Leaf Diseases Identification using CNN

INFORMATION TECHNOLOGY

Crop diseases are key danger to food security. The speedy identification of the diseases are still difficult in many proportions of the world. This is because of lack of proper infrastructure. Now a days smart phone usage is increased and computer vision technology is also increased. Because of this smartphone assisted disease identification is possible using Deep Learning. we are developing web application to identify the disease of leaf using CNN. Public dataset contains 10000 images contains both infected and healthy tomato leaves. They are collected under controlled conditions. In the proposed method deep learning is used to detect the disease of the leaves. A deep convolutional neural network model is trained to identify these diseases. In this we have used inception v3 model. The trained achieved 88.34% accuracy on the test dataset. This method is more feasible to detect the diseases in tomato leaves.

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