The identification of corn leaf diseases based on transfer learning and data augmentation
Rongjie Hu, Shan Zhang, Peng Wang, Guoming Xu, Daoyong Wang, Yuqi Qian
Abstract
Corn is one of the most important food crops in the world, but there are many kinds of corn diseases, and it is difficult to diagnose by planting personnel based on experience. However, the misdiagnosis reduces production efficiency. With the development of computer technology, the use of deep learning and image recognition technology for plant disease detection has become an important research direction. We propose a convolutional neural network based on data augmentation combined with transfer learning to identify corn leaf disease models. The algorithm first increases the data by means of data augmentation to improve the generalization and accuracy of the model, and builds a convolutional neural network model based on transfer learning. Then, it uses the model for training, accelerates the training process of the convolutional neural network, and uses test dataset feedback network training results. In this study, the corn leaf images in PlantVillage were used as the dataset of our experiment to classify the four categories which consists of Corn Gray leaf spot, Corn Common rust, Corn Northern Leaf Blight and healthy leaves. We first obtained our optimization model by fine-tuning the GoogLeNet pre-training network, adjusting parameters such as optimizer and learning rate. Then we trained the optimization model, like the original GoogLeNet network, ResNet18, Vgg16, and Vgg19 networks based on transfer learning and compare the results. The results show that by using our optimized model, the average recognition accuracy of corn disease which consists of corn common leaf rust, Corn Common rust, Corn Northern Leaf Blight and healthy leaves reached 97.6%, and the recognition accuracy of each category was greater than 95%. Compared with the original GoogLeNet model, the highest accuracy rate is improved by 5.9%. Also, the effect is better when compared with other networks based on transfer learning. Our model provides new ideas for the identification of diseases, insect pests of corn and other crops.