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| Determination of Conductivity in Sweet Corn Seeds with Algorithm of GA and SPA Based on Hyperspectral Imaging Technique |
| ZHANG Ting-ting1, ZHAO Bin1, YANG Li-ming2, WANG Jian-hua1, SUN Qun1* |
1. Department of Plant Genetics and Breeding, College of Agronomy and Biotechnology, The Innovation Center (Beijing) of Crop Seed Sciences of Ministry of Agriculture, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
2. College of Science, China Agricultural University, Beijing 100083, China |
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Abstract The vigor of seeds plays a vital role to the agricultural development. But the low vigor and storage-tolerance seeds are common problems for sweet corn. Therefore, it has a certain practical significance to detect the sweet corn seed vigor accurately and timely. Electrical conductivity test is a traditional method of determining the vigor ofseeds. However, it is a labor-intensive, time-consuming, and destructive process, which is subject to human error. Given that, this study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging (HSI) technique to detect the electrical conductivity of sweet corn seeds. Sweet cornseeds treated by high temperature and high humidity aging were prepared as experimental materials. The visible and near-infrared hyperspectral imaging acquisition system (400~1 000 nm) was constructed to acquire the hyperspectral images of the sweet corn seeds. After HSI spectra collection, electrical conductivity tests were conducted in sweet corn seeds. The average reflectance data of the region of interest were extracted for spectral characteristics analysis. Then different pre-processing algorithms including standard normal variate (SNV), first derivative (FD), second derivative(SD), multiplicative scatter correction (MSC) were conducted to build partial least squares regression (PLSR) models of the conductivity. Lastly, the hyperspectral effective wavelengths related to conductivity of sweet corn seeds were extracted by SPA and GA for PLSR models. The results showed that the best pre-processing algorithm was MSC method. The SPA was not performing as well as GA which selected only 25 characteristic wavebands from the all 853spectral wavebands. The PLSR model built by using MSC and GA exhibited the optimal performance with correlation coefficient of 0.976 and 0.973 for calibration set and prediction set, respectively, and root mean squared error for calibration and prediction were 0.194 and 0.212. The results indicated that combining the visible and near-infrared hyperspectral imaging technique with MSC-GA-PLSR can be used as a feasible and reliable method for the determination of conductivity in sweet corn seeds. The result can provide a theoretical foundation for rapid detection of seed conductivity using spectral information.
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Received: 2018-06-27
Accepted: 2018-11-08
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Corresponding Authors:
SUN Qun
E-mail: sqcau@126.com
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