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

机译:用高光谱成像技术快速预测和可视化含水分含量的水分含量

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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.
机译:使用具有互补光谱范围的两个高光谱成像(HSI)系统无组织地检测黄瓜种子的水分含量(MC)。分别从400-1000和1050-2500nm中从高光盘图像中提取每种黄瓜种子的平均光谱,发现反射光谱随着MC在1050-2500nm的增加而降低。校准模型由部分最小二乘回归(PLS)建立,以分析预处理和波长选择方法的预测能力。通过Savitzky-golay平滑和标准正常变化(SG-SNV)预处理的400-1000nm的光谱和通过SG标准化预处理的1050-2500nm光谱产生了更好的结果。通过三个有效的波长选择方法获得最佳波长,即竞争自适应重新加权的采样(CARS),迭代地保留信息性变量(IRIV)和随机青蛙(RF)。随后,通过所选波长分别构建简化模型。与其他开发的模型相比,在1050-2500nm处由RF选择的八个波长建立的校准模型实现了最佳性能。预测的相关系数(R-pre)为0.917,预测的根均方误差(RmSep)为1.656%。最后,在像素电平产生MC分布的可视化。本作工作中获得的结果表明,在黄瓜种子中施加HSI技术以测量MC是可行的,1050-2500区的光谱比MC检测更高于400-1000。 HSI提供的MC分布的可视化确保了单种子水平的MC综合评估。所选波长可用于构建多光谱成像系统以检测黄瓜种子的Mc,这可能会使高CM的种子摆脱,避免储存期间的种子劣化。

著录项

  • 来源
    《Infrared physics and technology》 |2019年第2019期|共9页
  • 作者单位

    Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China;

    Beijing Acad Agr &

    Forestry Sci Beijing Key Lab Vegetable Germplasm Improvement Beijing Vegetable Res Ctr Beijing 100097 Peoples R China;

    Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China;

    Beijing Acad Agr &

    Forestry Sci Beijing Key Lab Vegetable Germplasm Improvement Beijing Vegetable Res Ctr Beijing 100097 Peoples R China;

    Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China;

    Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China;

    Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 红外线;红外技术及仪器;
  • 关键词

    Hyperspectral imaging; Cucumber seeds; Moisture content; Visualization;

    机译:高光谱成像;黄瓜种子;水分含量;可视化;

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