Research on detection method of moldy tobacco leaf raw materials based on hyperspectral and machine learning

Using hyperspectral imaging technology and machine learning methods to classify and identify whether tobacco leaves have undergone mold contamination. Visible-near-infrared hyperspectral imaging technology was employed, and various preprocessing techniques such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), and convolutional smoothing (SG) were applied to preprocess the spectral data. Feature wavelength selection was carried out through successive projections algorithm (SPA) and principal component analysis loadings (PCA loadings). Classification models were built using random forest (RF), Softmax, and support vector machine (SVM).Among the preprocessing methods, SNV was identified as the optimal spectral preprocessing technique. The RF model established through feature wavelength selection using SPA demonstrated the best performance, with training and testing accuracies reaching 98.82% and 98.64%, respectively. The combination of hyperspectral imaging technology with the SPA-RF model proved to be effective in accurately classifying and identifying mold contamination in tobacco leaves.