Quality Assessment of Crop Seeds by Near-Infrared Hyperspectral Imaging

文献类型： 外文期刊

第一作者： Zhu, Dazhou

作者： Zhu, Dazhou;Wang, Kun;Wang, Cheng;Zhu, Dazhou;Zhang, Dongyan;Huang, Wenjiang;Yang, Guijun;Wang, Cheng;Ma, Zhihong

作者机构：

关键词： Hyperspectral Imaging; Near Infrared; Genetic Algorithm; Wheat; Soybean; Seed

期刊名称：SENSOR LETTERS （ 影响因子：0.558； 五年影响因子：0.58 ）

ISSN： 1546-198X

年卷期： 2011 年 9 卷 3 期

页码：

收录情况： SCI

摘要： Non-destructively analyzing the quality of crop seeds is very important for early generation screening in crop breeding. In this study, winter wheat and soybean seeds were measured by a near-infrared (NIR) pushbroonn hyperspectral imaging system. Hyperspectral imaging has advantages over conventional NIR spectroscopy by providing both spectral and spatial information simultaneously. The reflectance spectral images were collected at 850-1700 nm with a resolution of 2.7 nnn. The spectrum of each sample was extracted from the data cube using image processing method. Partial least square regression (PLSR) was then used to construct the calibration models. For the determination of crude protein of winter wheat, the correlation coefficient of calibration was r = 0.973, the standard deviation of prediction was SEP = 0.556, and the relative of SEP was SEP% = 3.399%. For the determination of crude protein and crude fat of soybean, the results were r = 0.902, SEP = 1.332, SEP A, = 3.195% and r = 0.901, SEP = 0.613, SEP% = 3.148%, respectively. The results showed that NIR hyperspectral imaging could accurately evaluate the component of grain seeds. The extracted spectra from different seed positions and the gaps between them have significant difference. The data of hyperspectral image contained a lot of redundant information. Therefore, genetic algorithm (GA) was applied to select sensitive wavelengths for hypercube. The results showed that GA did not significantly improve the model performance; however, it could simplify the calculations. Moreover, based on the selected sensitive wavelengths, the low-cost multi-spectral imaging system could be developed specially for the quality assessment of wheat or soybean seeds. It was concluded that hyperspectral imaging was useful for the quality assessment of breeding materials and had potential application for assisting crop breeding.

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