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18 October 2022 Nondestructive identification of maize varieties using near infrared spectroscopy combined with machine learning
Juanhua Zhu, Yuan Zhang, Mengchu Song, Ang Wu
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Proceedings Volume 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022); 1234918 (2022) https://doi.org/10.1117/12.2657286
Event: International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 2022, Zhengzhou, China
Abstract
It is difficult to distinguish different maize varieties accurately by naked eyes or simple instruments because of their similar appearance. However near-infrared spectroscopy (NIRS) provides a quick, accurate and non-destructive way to analyze of maize varieties. Near-infrared spectral images of four types of maize seeds are collected in the wavelength range of 1100nm-1900nm. The near-infrared spectra are preprocessed by standard normalization transform and wavelength preselection. Eighty near-infrared spectra are obtained for each type of maize seeds, 60 of which are randomly selected as the training set and the remaining 20 spectra as the testing set. Then, support vector machine (SVM), SVM-Genetic algorithm (SVM-GA), backpropagation (BP) neural network, and principal component analysis-K nearest neighbor algorithm (PCA-KNN) are established for the variety identification. The average accurate identification rates are 93.75%, 97.50%, 92.50% and 87.50% respectively. The SVM-GA model shows better performance for near-infrared spectra of maize seeds, and the classification accuracy increases nearly by 4%. This work provides an effective method for rapid automatic classification of maize varieties.
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Juanhua Zhu, Yuan Zhang, Mengchu Song, and Ang Wu "Nondestructive identification of maize varieties using near infrared spectroscopy combined with machine learning", Proc. SPIE 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 1234918 (18 October 2022); https://doi.org/10.1117/12.2657286
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KEYWORDS
Near infrared spectroscopy
Spectroscopy
Machine learning
Light sources
Nondestructive evaluation
Optical fibers
Proteins
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