基于高光谱技术的采摘期烟叶水分含量研究

烟叶含水量的快速检测在烟草种植业中起着关键的作用,检测采摘期烟叶水分含量,对烟草工艺具有重要意义.为了快速、无损地检测采摘期烟叶水分含量,提出一种主成分分析(PCA)结合马氏距离算法(MD)的方法来剔除异常样本,再使用偏最小二乘法(PLS)估测采摘期烟叶水分含量.首先,利用GaiaSky-mini2机载高光谱成像仪获取...

Study on the moisture content of tobacco leaves during the picking period based on hyperspectral technology

It is of great significance to detect the moisture content of tobacco leaves during the picking period, which plays a critical role in the tobacco planting industry. In order to rapidly and nondestructively detect the moisture content of tobacco leaves, a method of principal component analysis (PCA) combined with Mahalanobis distance (MD) was proposed to eliminate the abnormal samples. Then partial least squares (PLS) was used to estimate the moisture content of tobacco leaves. Firstly, the hyperspectral data of 141 mature tobacco leaves were obtained by Gaiasky mini2 airborne hyperspectral imager. The original spectra were preprocessed by multiple scattering correction (MSC), standard normal variable exchange (SNV), and Savitzky Golay convolution smoothing. In addition, the combination of PCA and MD was used to eliminate the abnormal samples in the calibration sample set. Finally, PLS was used to establish the moisture content analysis model of tobacco leaves based on the data set after removing samples. The results showed thatthe PCA MD PLS model preprocesseffectt. The established PLS model has the best predictive ability for tobacco moisture content. The correlation coefficient of the prediction modelis 0.852 7, and the mean square error is 1.376 6.