基于高光谱的脱绒棉种电导率快速检测研究

为了寻求一种快速、无损检测脱绒棉种活力的方法，提出基于高光谱技术预测脱绒棉种电导率。采集了新陆早50、新陆早57、新陆早62三个品种且不同老化程度下共810粒脱绒棉种高光谱图像(400~1 000 nm)，通过组合不同预处理方法，采用chauvenet检测方法剔除异常值后建立了偏最小二乘法(PLS)、逐步多元线性回归(SMLR)、主成分回归(PCR)模型。结果表明，采用变量标准化(SNV)、卷积平滑(Savitzky-Golay)、一阶微分(First derivative)和norris微分平滑组合的预处理方法，波段范围为480~530，650~980 nm下建立的PLS模型效果最佳；其中PLS模型得到新陆早50、新陆早57、新陆早62的预测集相关系数和校正集相关系数分别为0.88，0.90，0.92，0.91，0.89，0.90；预测集均方根误差(RMSEP)和校正集均方根误差(RMSEC)分别为44.3，38.4，37.8，46.5，43.5和40.8 μS·cm-1。研究结果表明，采用高光谱技术预测脱绒棉种电导率具有一定的可行性，也为其他种子的活力检测奠定了良好的基础。

Study on the Rapid Detection of Delinted Cottonseeds Conductivity with Hyperspectral Imaging Technique

In order to seek a method which can detect delinted cottonseeds vigor rapidly and non-destructively.Hyperspectral imaging is an emerging technique that is applied in detection of agricultural products in recent years.Experiments of three varieties of delinted cottonseeds with different aging degree were conducted,including Xin Luzao 50,Xin Luzao 57,Xin Luzao 62.The hyperspectral image of 810 grain of delinted cottonseeds in the range of 450~1 000 nm was collected with hypersectral imaging system.Different pretreatment methods were combined and chauvenet detection method excluding outlier was applied to establish a partial least squares (PLS) ,Stepwise Multiple Linear Regression (SMLR),Principal Component Regression (PCR) model.The results shows that the best combination of two kinds of pretreatment methods were standard normal variate (SNV),Savitzky-Golay (S-G) smoothing,First derivative and standard normal variate (SNV),First derivative,Norris Smoothing.After preprocessing,combined with the range of 480~530,650~980 nm to establish three different kinds of models.The partial least squares (PLS) model effect is the best.As for the prediction set and calibration set of PLS model of conductivity,Xin Luzao 50,Xin Luzao 57,Xin Luzao 62,correlation coefficient of prediction set and correlation coefficient of calibration set were 0.92,0.95,0.92,0.90,0.89,0.90.Xin Luzao 50,Xin Luzao 57,Xin Luzao 62 root mean squared error of prediction (RMSEP) and root mean squared error of calibration (RMSEC) were 44.3,38.4,37.8,46.5,43.5 and 40.8 μS·cm-1,respectively.This paper studied the applicaiton of hyperspectral image technology to detect the vigor of delinted cottonseeds,not only provides a new method for detecting delinted cottonseed vigor,but also lays a theoretical foundation for other seed vigor test.