Abstract

Maize is the main cereal crop in China. In the process of maize planting, the selection of suitable maize varieties is an important link to achieving a high yield. Because the appearance of maize seeds is very similar, it is difficult to accurately identify their species with the naked eye. In order to realize the rapid identification of different varieties of maize seeds, this paper proposes a rapid identification method of maize varieties based on near-infrared (NIR) spectroscopy coupled with locally linear embedding (LLE) and a support vector machine (SVM). The NIR data, preprocessed by multiple scattering correction (MSC), were dimensionally reduced by LLE, a principal component analysis (PCA), and isometric mapping (Isomap), and combined with SVM to establish a maize variety identification model. The results show that the LLE-SVM model has the best performance, whose classification accuracy and kappa coefficient of the test set can reach 100% and 1.00. The classification accuracy and kappa coefficient of the LLE-SVM model are better than the PCA-SVM model and Isomap-SVM model. Therefore, LLE can reduce the complexity of the model and improve the accuracy of the model. It can be used for the rapid identification of maize varieties and provide a new idea for the classification and identification of other agricultural products.

© 2022 Optica Publishing Group

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

Lili Xu, Jinming Liu, Chunqi Wang, Zhijiang Li, and Dongjie Zhang
Appl. Opt. 62(11) 2756-2765 (2023)

Automatic rock classification of LIBS combined with 1DCNN based on an improved Bayesian optimization

Guangdong Song, Shengen Zhu, Wenhao Zhang, Binxin Hu, Feng Zhu, Hua Zhang, Tong Sun, and Kenneth TV Grattan
Appl. Opt. 61(35) 10603-10614 (2022)

Construction of classification models for pathogenic bacteria based on LIBS combined with different machine learning algorithms

Haorui Sun, Canran Yang, Youyuan Chen, Yixiang Duan, Qingwen Fan, and Qingyu Lin
Appl. Opt. 61(21) 6177-6185 (2022)

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

Changhao Bao, Changhao Zeng, Jinming Liu, and Dongjie Zhang
Appl. Opt. 61(19) 5790-5798 (2022)

Identification of wheat quality using THz spectrum

Hongyi Ge, Yuying Jiang, Zhaohui Xu, Feiyu Lian, Yuan Zhang, and Shanhong Xia
Opt. Express 22(10) 12533-12544 (2014)