Abstract:

 In order to identify chronic, acute, brown dot and white dot, four types of rice blast diseases early and accurately, a deep neural network is constructed by combining sparse automatic encoder and SPSO-SVM (Switching Particle Swarm Optimization Support Vector Machine). Compared with other algorithms, the neural network needs to input a large number of images, the autoencoder can extract the most representative information in the original image, reduce the amount of information in the input, and then put the reduced information into the neural network to learn, greatly reducing the difficulty and time of learning. Firstly, the input data is encoded, decoded and reconstructed by sparse automatic encoder to learn the hierarchical features of rice blast leaf spots, and the sparse condition constraint is added to the automatic encoder to compress the hidden layer, so as to learn the higher-level hidden features. Secondly, the support vector machine optimized by switching particle swarm optimization is used to identify the types of rice blast. The open Kaggle rice disease image database and the actually collected rice blast image are used as the data set. 350 images of each type were selected to form samples, and each image is normalized to 4096 dimensional vector. 80% of the samples are randomly selected as the training set and the remaining 20% are used as the test set. Through 10 cross validation, the average recognition accuracy of the test set is 95. 7% . The experimental results show that the proposed method can effectively identify the early disease of rice leaf blast from the features of disease spots, which is of great significance for the early prevention of rice blast.

Key words: rice blast')">

rice blast, early disease, sparse automatic encoder, switching particle swarm optimization, support vector