The terahertz (THz) spectra in the range of 0.2-1.6 THz (6.6-52.8 cm^-1) of wheat grains with various degrees of deterioration (normal, worm-eaten, moldy, and sprouting wheat grains) were investigated by terahertz time domain spectroscopy. Principal component analysis (PCA) was employed to extract feature data according to the cumulative contribution rates; the top four principal components were selected, and then a support vector machine (SVM) method was applied. Several selection kernels (linear, polynomial, and radial basis functions) were applied to identify the four types of wheat grain. The results showed that the materials were identified with an accuracy of nearly 95%. Furthermore, this approach was compared with others (principal component regression, partial least squares regression, and back-propagation neural networks). The comparisons showed that PCA-SVM outperformed the others and also indicated that the proposed method of THz technology combined with PCA-SVM is efficient and feasible for identifying wheat of different qualities.

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Terahertz (THz) radiation (0.1~10 THz) shows great potential in agricultural products detection, biomedical, and security inspection in recent years. Machine learning methods are widely used to support the user demand of higher efficiency and high prediction accuracy. The technological and key challenges of machine learning methods are for THz spectroscopy and image data preprocessing, reconstruction algorithms, and qualitative and quantitative analysis. In this paper, an exhaustive review of recent related works of THz detection and imaging techniques and machine learning methods are presented. The application of machine learning methods combined with THz technology in quality inspection of agricultural products, biomedical, security inspection, and materials science are highlighted. Challenges of machine learning methods for these applications are addressed. The development trend and future perspectives of THz technology are also discussed.INDEX TERMS terahertz spectrum, terahertz imaging, machine learning, agricultural products, detection application.

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.