Yang Yan
ABSTRACT
Blast rice is a biological disaster in rice cultivation. Once it happens, it will reduce production at least up to 40-50%. In this study, the near-infrared hyper-spectral image was used to early detect blast rice at seedling stage. Samples were divided into two classes: infected samples and healthy samples. All of samples were imaged using Near-infrared hyper-spectral imaging system(900-1700nm). In order to detect disease, principal component analysis (PCA) was applied and linear discriminant analysis (LDA) model was built. The classification accuracy and precision of PCALDA model reach 0.92 and 0.862 on validation set. Meanwhile, five feature wavelengths (1188nm, 1339nm, 1377nm, 1432nm, 1614nm) were found and PCALDA classification model base on feature images was also built and discussed. The result showed it feasibility to early detect Rice Blast (Pyricularia) at seedling stage based on Near-infrared Hyper-Spectral Images in a quick and easy way.
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Index Terms
- Early Detection of Rice Blast (Pyricularia) at Seedling Stage based on Near-infrared Hyper-spectral Image
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