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A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture
Author(s) -
Yang Ning,
Yu Junjie,
Wang Aiying,
Tang Jian,
Zhang Rongbiao,
Xie Liangliang,
Shu Fangyu,
Kwabena Oppong Paul
Publication year - 2020
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.10383
Subject(s) - fingerprint (computing) , identification (biology) , texture (cosmology) , spore , artificial intelligence , computer science , diffraction , blast disease , plant disease , pattern recognition (psychology) , microbiology and biotechnology , biology , botany , physics , optics , image (mathematics) , biochemistry , gene
BACKGROUND Rice blast fungus is a worldwide 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. Manual 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; this method 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 manual 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 manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION 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 manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry