数字农科院2.0

Rapid Detection Of Rice Disease U.sing Microscopy Image Identification B ased On The Synergistic Judgment Of Texture And Shape Features And Decision Tree-Confusion Matrix Method

文献类型： 外文期刊

作者： EL:Mesery, Hany S.;Yang, Ning;Qian, Yong;Wang, Aiying;Tang, Jian;Zhang, Rongbiao

作者机构：

关键词： rice disease; image processing; feature extraction; decision tree; confusion matrix

期刊名称： JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE

ISSN： 0022-5142

年卷期： 2019 年 99 卷 14 期

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摘要： BACKGROUND Rice smut and rice blast are listed as two of the three major diseases of rice. Owing to the small size and similar structure of rice blast and rice smut spores, traditional microscopic methods are troublesome to detect them. Therefore, this paper uses microscopy image identification based on the synergistic judgment of texture and shape features and the decision tree-confusion matrix method. RESULTS The distance transformation-Gaussian filtering-watershed algorithm method was proposed to separate the adherent rice blast spores, and the accuracy was increased by about 10%. Four shape features (area, perimeter, ellipticity, complexity) and three texture features (entropy, homogeneity, contrast) were selected for decision-tree model classification. The confusion-matrix algorithm was used to calculate the classification accuracy, in which global accuracy is 82% and the Kappa coefficient is 0.81. At the same time, the detection accuracy is as high as 94%. CONCLUSIONS The synergistic judgment of texture and shape features and the decision tree-confusion matrix method can be used to detect rice disease quickly and precisely. The proposed method can be combined with a spore trap, which is vital to devise strategies early and to control rice disease effectively. (c) 2019 Society of Chemical Industry

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