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Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture
Ning Yang1, Yong Qian1, Hany S El-Mesery2
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
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.
Insights
This study introduces a new method for identifying rice blast and smut spores using image analysis. The technique combines texture and shape features with a decision tree model for accurate disease detection.
Area of Science:
- Agricultural Science
- Plant Pathology
- Image Analysis
Background:
- Rice blast and rice smut are major rice diseases.
- Microscopic identification is challenging due to spore size and similarity.
- Need for efficient and precise detection methods.
Purpose of the Study:
- To develop an automated method for detecting rice blast and smut spores.
- To improve the accuracy and efficiency of rice disease diagnosis.
- To integrate texture and shape features for synergistic judgment.
Main Methods:
- Microscopy image identification using texture and shape features.
- Distance transformation-Gaussian filtering-watershed algorithm for spore separation.
- Decision tree model with confusion matrix for classification.
Main Results:
- Spore separation accuracy improved by approximately 10%.
- Selected features: area, perimeter, ellipticity, complexity (shape); entropy, homogeneity, contrast (texture).
- Global accuracy of 82% (Kappa: 0.81), detection accuracy of 94%.
Conclusions:
- Synergistic texture and shape features with decision tree-confusion matrix enable rapid and precise rice disease detection.
- Method can be combined with spore traps for early strategy development.
- Effective control of rice diseases is facilitated by this approach.
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