关键词: 含水量, 烟叶, 高光谱, 主成分分析, 马氏距离, 偏最小二乘法

Abstract:  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.

Key words: water content, tobacco leaf, hyperspectral, principal component analysis, Mahalanobis distance, partial least squares method