Abstract
Purpose
The aim of this study was to find the optimal detection method for cucumber powdery mildew and improve the identification efficiency.
Methods
Image segmentation technology was used to extract spot images and grade classification of powdery mildew. The relationship between powdery mildew spot and spectral reflectance and intensity was studied. The powdery mildew detection model was established by using qualitative analysis and quantitative prediction methods combined with greenness (a*) indices of cucumber leaves.
Results
There were strong positive correlations between greenness and spectrum in some characteristic bands. Through the extraction of disease spot images and disease classification, it was found that the higher the disease grade of leaves was, the higher the spectral reflectivity and fluorescence intensity. In the quantitative prediction model, the R2 of the NIR spectrum was improved (0.8742) after MSC and SPA, and the effect of the fluorescence spectrum model was not ideal. In the qualitative discriminant model, KNN and ensemble subspace discriminant were obtained for two kinds of spectra, and the identification accuracy of the qualitative model was 97.5% after verification.
Conclusion
An NIR spectral model can be used for the quantitative prediction of cucumber powdery mildew. The qualitative discriminant model had high accuracy for cucumber powdery mildew.
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Funding
This study was funded by the LiaoNing Revitalization Talents Program (XLYC2007043) and the Scientific Research Fund Project of Liaoning Province ( LJKZZ20220087).
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Xu, J.T., Zhang, Z., Guo, Y.H. et al. Detection of Cucumber Powdery Mildew Based on Spectral and Image Information. J. Biosyst. Eng. 48, 115–122 (2023). https://doi.org/10.1007/s42853-023-00178-w
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DOI: https://doi.org/10.1007/s42853-023-00178-w