Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System.
作者： Yangyang Fan , Tao Wang , Zhengjun Qiu , Jiyu Peng , Chu Zhang
DOI: 10.3390/S17112470
关键词: Rice plant 、 Chilo suppressalis 、 Chemometrics 、 Seedling 、 Mathematics 、 Infestation 、 Visible near infrared 、 Hyperspectral imaging 、 Remote sensing 、 Principal component analysis
摘要: Striped stem-borer (SSB) infestation is one of the most serious sources damage to rice growth. A rapid and non-destructive method early SSB detection essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used detect in identify degree (DI). Visible/near-infrared images (in spectral range 380 nm 1030 nm) were taken healthy plants infested by 2, 4, 6, 8 10 days. total 17 characteristic wavelengths selected from data extracted successive projection algorithm (SPA). Principal component analysis (PCA) applied images, 16 textural features based on gray-level co-occurrence matrix (GLCM) first two principal (PC) images. back-propagation neural network (BPNN) establish evaluation models full spectra, wavelengths, fusion, respectively. BPNN a fusion achieved best performance, classification accuracy calibration prediction sets over 95%. The each satisfactory, samples 2 days slightly low. all, study indicated feasibility techniques degrees infestation.
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