25 October 2016 Variable selection based cotton bollworm odor spectroscopic detection
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101570W (2016) https://doi.org/10.1117/12.2244934
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
Aiming at rapid automatic pest detection based efficient and targeting pesticide application and shooting the trouble of reflectance spectral signal covered and attenuated by the solid plant, the possibility of near infrared spectroscopy (NIRS) detection on cotton bollworm odor is studied. Three cotton bollworm odor samples and 3 blank air gas samples were prepared. Different concentrations of cotton bollworm odor were prepared by mixing the above gas samples, resulting a calibration group of 62 samples and a validation group of 31 samples. Spectral collection system includes light source, optical fiber, sample chamber, spectrometer. Spectra were pretreated by baseline correction, modeled with partial least squares (PLS), and optimized by genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS). Minor counts differences are found among spectra of different cotton bollworm odor concentrations. PLS model of all the variables was built presenting RMSEV of 14 and RV2 of 0.89, its theory basis is insect volatilizes specific odor, including pheromone and allelochemics, which are used for intra-specific and inter-specific communication and could be detected by NIR spectroscopy. 28 sensitive variables are selected by GA, presenting the model performance of RMSEV of 14 and RV2 of 0.90. Comparably, 8 sensitive variables are selected by CARS, presenting the model performance of RMSEV of 13 and RV2 of 0.92. CARS model employs only 1.5% variables presenting smaller error than that of all variable. Odor gas based NIR technique shows the potential for cotton bollworm detection.
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Chengxu Lü, Shasha Gai, Min Luo, Bo Zhao, "Variable selection based cotton bollworm odor spectroscopic detection", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101570W (25 October 2016); doi: 10.1117/12.2244934; https://doi.org/10.1117/12.2244934
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