# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
###### Abstract  
Seed purity has an important impact on the yield and quality of maize. Studying the spectral characteristics of hybrid maize and exploring the rapid and non-destructive detection method of seed purity are conducive to the development of maize seed breeding and planting industry. The near-infrared spectral data of five hybrid maize seeds were collected in the laboratory. After eliminating the obvious noises, the multiple scattering correction (MSC) was applied to pretreat the spectra. PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Signalic were used as pattern recognition methods to classify five different types of maize seeds. The recognition accuracy of the models established by different algorithms was 84.4%, 97.6, 100%, 96.4, 99.2%, 100%, 98.4%, and 91.2%, respectively. The results indicated that hyperspectral imaging technology could be used for variety classification and the purity detection of maize seeds. To improve the calculation speed, using the principal component analysis (PCA) to reduce the dimension of hyperspectral data, we then established classification models based on characteristic wavelengths. The recognition accuracy of the models established by different algorithms was 80.8%, 86.8%, 98%, 94%, 96.8%, 98.4%, 94.4%, and 88.2%, respectively. The results showed that the selected sensitive wavelengths could be used to detect the purity of maize seeds. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the variety identification and purity detection of maize seeds. This study also provides a new method for rapid and non-destructive detection of seed purity. hyperspectral imaging; purity testing; PCA; machine learning; SVM Article Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize  
[PERSON], [PERSON], [PERSON], [PERSON], Y. Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize  
[PERSON], [PERSON], [PERSON], [PERSON], Y. These methods generally take a long time and require professional personnel and special instruments. However, the test results are greatly affected by the subjective experience of the test personnel. Therefore, developing rapid, accurate, and non-destructive methods for classifying and identifying maize seeds is important in the seed industry. In recent years, with the development of spectroscopy technology and image processing technology, near-infrared spectroscopy (NIRS) has been widely studied and deeply used in seed purity and quality detection [9; 10; 11; 12; 13]. In the field of grain research, researchers used near-infrared spectroscopy technology to study the material content, vigor, moisture, authenticity, and defect seed detection of seeds. [PERSON] et al. used the random forest to establish the rice seed variety recognition model, and the model accuracy based on the characteristic wavelength exceeded 80% [14]. [PERSON], [PERSON], and others used ML and SVM for farmland target classification, and the total classification accuracy exceeded 97.5%, while the SVM-M model can reach 99.5% [15]. [PERSON] et al. conducted convolution smoothing pretreatment on the spectrum of soybeans and identified soybean varieties through the character wavelength strengths and limit learning machine with an accuracy of 78.22% [16]. These studies revealed that the hyperspectral imaging method was feasible for the classification and identification of seeds. In terms of classification and detection of maize seeds, [PERSON] et al. used the characteristic wavelengths selection method based on standard deviation, combined with PCA and LDA to reduce the dimension of the data, and established an identification model for the female parent and its hybrid varieties of maize. The correct identification rate reached more than 90% [17]. [PERSON] et al. used near-infrared spectroscopy to detect and identify transgenic maize and obtained good recognition results [18]. [PERSON] et al.