LAUSR

BACKGROUND Rice blast is a worldwide rice fungus disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious and the speed of transmission is fast. Artificial identification is time-consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied, which utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of artificial identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional artificial recognition of microscope (increased by more than 0.3%), but also faster after the neural network training (increased by more than 90%). The diffraction recognition based on crop disease spores' diffraction fingerprint texture can be completed in a few seconds recognition. In this paper, the diffraction identification method is adopted, and its test accuracy is 97.18%. CONCLUSION It can be concluded that the proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing artificial identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. This article is protected by copyright. All rights reserved.