A Tomato Leaf Diseases Classification Method Based on Deep Learnings

In the process of planting crops, the detection of diseases in the leaf parts is one of the key links to the prevention and control of crop diseases. This paper takes tomato leaves as experimental objects, and uses the deep learning method to extract the disease features on leaf surface, including three most common species (Spot blight, Late blight and Yellow leaf curl disease). After continuous iterative learning, the network can predict the category of each disease picture. For each of the three diseases, 1000 pictures were selected, divided into 900 pictures for training set (2700 in total) and 100 pictures for test set (300 in total). The experiment takes Resnet-50 as the basic network model. For comparison, the activation function of the network was changed into Leaky-ReLU and the kernel size of the first convolutional layer was modified to 11×11. After the improvement, the training accuracy in training set is 98.3% (increased by 0.6%) and the test accuracy in test set is 98.0% (increased by 2.3%).