Abstract：To solve the problems in traditional artificial crop disease prevention and reduction in large scale agriculture production, the deep learning algorithm was used to detect and recognize crop diseases. The 47 637 images were detected for disease identification with 10 species of tomatoes, potatoes and corn, 27 diseases and total 61 classes. Six currently popular deep network structures of Vgg16, ResNetV1101, InceptionV4, etc. were applied to perform the feature extraction. The loss function with cross entropy and regularization was adopted to conduct back propagation. The data set was divided according to four different cases, and the initialization and the transfer learning were used in the training procedure. Six network structures with different learning rates were compared in the experiment. The results show that the highest recognition rate of all 61 classes is 84.6% by the initialization learning while the highest rate is 86.1% by the transfer learning and appropriate learning rate. For three statuses of diseases, the recognition rate is 87.4%. For 28 diseases, the recognition rate is 98.2%, and the recognition rate of the 10 types of diseases is 99.3%.
2019, Vol. 40 
