Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree-confusion matrix method.

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. © 2019 Society of Chemical Industry.