基于高光谱成像技术的烤烟上部烟叶成熟度光谱特征分析及判别模型构建应用研究OA北大核心CSTPCD

[Objective]The purpose of this study was to investigate the hyperspectral characteristics of upper tobacco leaves at different-maturity levels and the feasibility of intelligent discrimination.[Methods]In this study,a portable hyperspectral instrument was used to collect the hyperspectral imagines of upper tobacco leaves of three different-maturity levels(pre-maturity(SS),maturity(CS)and post-maturity(GS))and extracted their spectral data.Their spectral characteristics were studied by using correlation analysis,principal components analysis and variation analysis,and 5 models(SVM,KNN,RF,LightGBM and XGBoost)were constructed for evaluating their discriminant performances of tobacco leaf maturity.[Results]The results showed that:(1)there was a strong correlation among the bands within the visible light(400-720 nm)or the near infrared(750-1000 nm)regions,while the correlation between the two regions was weak.(2)the 5 principal components(PC1-PC5)with eigenvalues greater than 1 almost contained all the hyperspectral information.The spectral reflectance characteristics of upper tobacco leaves with different maturity levels showed significant difference in visible light,red edge and part of near infrared region(950-1000 nm).(3)Among the 5 models,SVM has the best evaluation,with precision,recall and Fl scores for the samples in 2021 above 0.95,and for the samples in 2022 and 2021+2022 above 0.93 and 0.92 respectively.[Conclusion]The hyperspectral data of the upper tobacco leaves exhibit multicollinearity,which has excellent dimensionality reduction effects.Moreover,there are significant differences in spectral reflectance characteristics at different maturity levels.The SVM discriminant performance has good stability across different years and can be used for determining the maturity of upper tobacco leaves.