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-Sigmaid 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.

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

“…This inherent randomness in the algorithm allows it to effectively address the issue of overfitting and improve the model's ability to generalize well to unseen data. The accuracy of the results in random forest models is influenced by the number of decision trees (ntree) and the number of randomly selected attributes for splitting (mtry) [33].…”