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.
In this paper, a high-speed hyperspectral target detection system based on high-efficiency spectrographs and illumination devices is proposed. The system includes a hyperspectral imager, an illumination system, and a data processing system with spectral target recognition. The system can be used for fast impurity rejection on industrial lines. By adopting a high diffraction efficiency grating and a low distortion spectral spectroscopic system, the system realizes spectral imaging with high throughput and low distortion. Compact linear light source is used to achieve high irradiance full-spectrum illumination. The edge computing system adopts a spectral target recognition method combining CTBS with RTCEM and RTRAD. The spectral range of the system is 400 nm to 1000nm, the spectral resolution is 5 nm. The system can be used for the assembly line with a transportation speed of 1m/s, and the unknown debris detection with an accuracy of more than 87% and the known debris detection with an accuracy of 96% can be carried out on debris with a size of less than 2 mm. The method proposed in this paper increases the detection speed of existing hyperspectral detection systems by more than three times, which is expected to improve the practicability of hyperspectral detection technology in the field of industrial production.
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