Image recognition of cotton leaf diseases and pests based on transfer learning

Abstract: The whole growth cycle of cotton is attacked by more 40 kinds of diseases and insect pests, which seriously affect its yield. Therefore, it is very important to identify the types of cotton diseases quickly and accurately and to control them timely and accurately to avoid the further spread of the disease and improve the yield of cotton. In view of the low accuracy of traditional cotton pest identification and the need for manual image feature extraction, a convolution neural network method is proposed to classify cotton leaf pests. Because it is difficult to obtain large data sets in the field of agriculture. Transfer learning and data enhancement are used to deal with small data sets. The network structure of 5 convolution layers, 2 full connection layers, and 1 Softmax classification layer were built based on the AlexNet model. The model was used to classify six diseases and insect pests in cotton leaves. The experiment was divided into 2 parts. The first part used to a PlantVillage big data set to learn the pre-training model on the build model as the feature extractor to save the model. Then, we used the transfer learning method of model transfer and fine-tuning parameters to train our model on the original cotton pest data set. The original cotton pest data set collected in the experiment was divided into training according to the proportion of 6:2:2 training set, verification set, and test set. The average test accuracy was 93.50% through 3 transfer learning training mechanisms (Freezing C1, C2, C3, C4, C5; freeze C1, C2, C3, C4, C5, and F6; freeze C1, C2, C3, C4, C5, and F6, F7). The second part used data enhancement technology to expand the original cotton pest data set to get a new set and then repeated the first part of the experiment with the new data set instead of the original data set. The cotton pest data was still divided in the proportion of 6:2:2, and then used two kinds of training mechanisms to transfer and learn (freeze C1, C2, C3, C4, C5; freeze C1, C2, C3, C4, C5, and F6), and the final average test set accuracy was 97.16%. Under the same experimental conditions, the accuracy of this model was much higher than that of traditional image classification methods, such as SVM and BP neural network. The experimental results of the deep convolution model VGG-19 and the GoogLeNet Inception v2 model showed that the improved model could converge faster and higher classification accuracy. The experimental results showed that the knowledge learned from the big data set of PlantVillage could be transferred to the target area (cotton pests data set) through the transfer learning, and the solution the problem of small data set could also make the model converge quickly. The method of data enhancement method could effectively alleviate the overfitting problem. This study had a good recognition rate for the pests of cotton leaves and provides a reference for the development of crop pest identification technology.