Rapid prediction and visualization of moisture content in single cucumber (Cucumis sativus L.) seed using hyperspectral imaging technology

文献类型： 外文期刊

第一作者： Xu, Yunfei

作者： Xu, Yunfei;Zhang, Chi;Li, Jiangbo;Xia, Yu;Fan, Shuxiang;Xu, Yunfei;Zhang, Haijun;Wu, Ping;Zhang, Chi;Li, Jiangbo;Fan, Shuxiang

作者机构：

关键词： Hyperspectral imaging; Cucumber seeds; Moisture content; Visualization

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2019 年 102 卷

页码：

收录情况： SCI

摘要： The moisture content (MC) of cucumber seeds was detected nondestructively using two hyperspectral imaging (HSI) systems with complementary spectral ranges. The mean spectrum of each cucumber seed was extracted from hyperspectral images in 400-1000 and 1050-2500 nm separately and it was found that the reflectance spectra decreased as the MC increased in 1050-2500 nm. Calibration models were established by partial least squares regression (PLS) to analyze the predictive ability of preprocessing and wavelength selection methods. The spectra in 400-1000 nm pretreated by Savitzky-Golay smoothing and standard normal variate (SG-SNV) and the 1050-2500 nm spectra pretreated by SG-normalization yielded better results. The optimal wavelengths were obtained by three effective wavelength selection methods, i.e., competitive adaptive reweighted sampling (CARS), iteratively retains informative variables (IRIV), and random frog (RF). Subsequently, the simplified models were built by the selected wavelengths separately. Compared to other developed models, the calibration model established with eight wavelengths chosen by RF from hyperspectral images at 1050-2500 nm achieved optimal performance. The correlation coefficient of prediction (R-pre) was 0.917 and the root mean square error of prediction (RMSEP) was 1.656%. Finally, the visualization of MC distribution was generated at the pixel level. The obtained results in this work indicated that applying HSI technology to measure MC in cucumber seeds was feasible, and the spectrum in 1050-2500 region was more promising than 400-1000 for MC detection. The visualization of MC distribution provided by HSI ensured comprehensive evaluation of MC in single seed level. The selected wavelengths were useful for building a multispectral imaging system to detect MC of cucumber seeds, which could get rid of the seeds with high MC and avoid seed deterioration during storage quickly.

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