基于高光谱成像技术的大麦种子水分含量测定

摘要：大麦种子水分含量直接关系到大麦种子的品质，快速无损检测大麦种子水分含量对农业生产有着重要意义。本研 究利用417～1000nm高光谱成像技术对大麦种子水分含量进行无损测定。对每颗大麦种子正反面进行高光谱成像，获取 高光谱立方体数据。采用5种预处理方法（（ 标准正态变量、多重散射校正、Savitzky-Golay平滑、归一化和一阶导数）对 光谱数据进行预处理。建立基于全波长测定大麦种子水分含量的支持向量回归模型和偏最小二乘回归模型。结果表明， 经过多重散射校正预处理的背面光谱数据效果更佳。为了简化建模过程、提高模型计算速度，采用两种有效的波长选取 方法（竞争自适应加权抽样和连续投影算法）选取特征波长，并基于选取的特征波长建立支持向量回归模型和偏最小二 乘回归模型。对比模型结果可知，基于连续投影算法选取的特征波长的支持向量回归模型取得了满意的效果，校准集的 判定系数为0.937，均方根误差为0.0154%，预测集的判定系数为0.883，均方根误差为0.0198%，残差预测偏差为2.596。 研究结果表明，利用高光谱成像技术测量大麦种子水分含量是可行的，并最终实现了水分含量分布的可视化。研究结果 为今后大麦种子水分含量的在线测定提供了理论依据。

关键词：大麦种子; 高光谱成像技术; 水分含量;可视化

期刊：SPECTROSCOPY LETTERS

DOI：https://DOI.org/10.1080/00387010.2020.1832531

设备：GaiaSorter

Determination of moisture content in barley seeds based on hyperspectral imaging technology

Abstract：：The moisture content of barley seeds is directly related to the quality of barley seeds. Rapid and nondestructive testing of the moisture content of barley seeds has an important signifi cance for agricultural production. In this study, the moisture content of barley seeds was determined nondestructively using hyperspectral imaging technology at 417–1000nm. Both sides of each barley seed were subjected to hyperspectral imaging to acquire hyperspectral cube data. The spectral data were pretreated by five preprocessing methods (standard nor mal variate, multiple scattering correction, Savitzky–Golay smoothing, normalization and first derivative). The support vector regression model and partial least squares regression model were established to determine moisture content in barley seeds based on full wavelengths. The results indicated that the dorsal spectral data pretreated by multiple scattering correc tion yielded better results. In order to simplify modeling process and improve calculation speed of the model, the feature wavelengths were selected by two effective wavelengths selection methods (competitive adaptive reweighted sampling and successive projections algorithm). Subsequently, the support vector regression model and partial least squares regression model were established based on these selected feature wavelengths. Comparing the results of models, the support vector regression model based on the feature wave lengths selected by successive projections algorithm generated a satisfied effect with deter mination coefficient of 0.937, root mean square error of 0.0154% for calibration set and determination coefficient of 0.883, root mean square error of 0.0198% and residual predic tion deviation of 2.596 for prediction set. The obtained results in this study indicated that applying hyperspectral imaging technology to measure moisture content in barley seeds was feasible. Finally, the visualization of moisture content distribution was generated. All these results provided a theoretical basis for the on-line determination of moisture content in barley seeds in the future.

Keywords：Barley seeds; hyperspectral imaging technology; moisture content; visualization