关键词: 卷积神经网络, 银杏轮纹病, 病患程度识别, BP神经网络

Abstract: Ginkgo biloba is a common economic tree in China. Digital identification of Ginkgo biloba diseases can improve the management level of ginkgo plantation industry, and provide possibility of early warning for diseases. Taking Ginkgo biloba ring disease as research objective, this paper carried out automatic identity on the degree of Ginkgo biloba disease by convolutional neural network as a classification algorithm. According to the leaf characteristics of Gingko biloba ring disease, 18-layer convolutional neural network was designed, including 4 feature extraction layers, 4 max-pooling layers and 2 fully connected layers. The experimental results showed that the identification rate of 5 different disease degrees is above 92%. The convolutional neural network designed in this paper was compared with the traditional BP neural network and Alex-Net, and the results showed that convolutional neural network possessed higher accuracy in identifying Ginkgo biloba disease degrees. This conclusion was of certain referential significance for identifying other diseases of Ginkgo biloba or other plant disease.

Key words: convolutional neural network, Ginkgo biloba ring disease, identification of disease degree, BP neural network