Abstract: Hyperspectral images of three parts （upper B, middle C and lower X） of flue-cured tobacco leaves were obtained by scanning with hyperspectral imaging technique （400~1 700 nm）, and their hyperspectral data were extracted. The hyperspectral characteristics of the three parts of tobacco leaves were studied by correlation analysis, principal component analysis and variance analysis, and five discriminant models （SVM, KNN, RF, LightGBM and XGBoost） for identifying tobacco leaf parts were constructed. The results showed that the spectral reflectance of the three parts of tobacco leaves was C>X>B （400~750 nm）, B>C>X （750~1 400 nm）, and C>B≈X （1 400~1 700 nm）. The hyperspectral data of the three parts of tobacco leaves had a strong correlation. In general, the correlation between the visible light and near-infrared bands was strong in their respective regions, while the correlation between the two was weak. A total of 7 principal components with eigenvalues greater than 1 were extracted, and the cumulative contribution rate of variance was close to 1.00. The spectral reflectance of the three parts of tobacco leaves was significantly different in 450~550 nm and 750~1 400 nm regions. The middle leaves had significant differences from the upper and lower leaves at 550~850 nm and 1 400~1 700 nm, respectively. The upper leaves had significant differences from the middle and lower leaves at 400~450 nm, respectively. The lower leaves had significant differences from the upper and middle leaves at around 680 nm. SVM performed best in distinguishing tobacco leaves in different parts, with accuracy, precision, recall and F1 scores all reaching above 95%, LightGBM performed in the middle, with various indicators between 90% and 95%, RF, KNN and XGBoost performed relatively poorly, with various indicators below 90%.

Key words: hyperspectral characteristics, flue-cured tobacco, model construction, position recognition