The multispectral imaging system captures images using multiple different light sources. As the camera focuses on different light sources, the wavelength of the light changes, causing a shift in the focal length of the camera and a corresponding change in the information captured by the image. To address this issue, this paper analyzes other evaluation functions and modifies the Tenengrad function to extract image gradient information from multiple directions. The paper then proposes the SIFTQuad_Tenen image clarity evaluation function, which is combined with the SIFT feature point extraction algorithm. Experiments were conducted using three different light sources: red, green, and blue. The resulting clarity evaluation curves and related indicators were compared with those of other evaluation functions. The results show that the proposed evaluation function has good performance in all three lighting scenarios, as well as better stability and higher sensitivity than other evaluation functions.
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.
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