Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System

Sensors
Yangyang FanYong He

Abstract

Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with ...Continue Reading

References

Aug 17, 2012·Plant Signaling & Behavior·Abdul Rashid WarHari Chand Sharma
Jul 6, 2014·Database : the Journal of Biological Databases and Curation·Chuanlin YinFei Li
Mar 10, 2016·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Ian T Jolliffe, Jorge Cadima
Aug 16, 2016·The Science of the Total Environment·Oksana SytarXiaolan He

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Methods Mentioned

BETA
imaging techniques
imaging technique
PCA

Software Mentioned

Matlab
ENVI
Spectral Image - V10E
SPA

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