Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms
1
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
3
Changchun Institute of Technology, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6980; https://doi.org/10.3390/s20236980
Received: 4 November 2020
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Revised: 1 December 2020
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Accepted: 4 December 2020
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Published: 7 December 2020
(This article belongs to the Special Issue Deep Learning for Nondestructive Detection and Analysis Using Hyperspectral Imaging)
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.
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Keywords:
hyperspectral imaging; correlation coefficient matrix; ensemble machine learning algorithms; random subspace linear discriminant
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MDPI and ACS Style
Wei, Y.; Li, X.; Pan, X.; Li, L. Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms. Sensors 2020, 20, 6980. https://doi.org/10.3390/s20236980
AMA Style
Wei Y, Li X, Pan X, Li L. Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms. Sensors. 2020; 20(23):6980. https://doi.org/10.3390/s20236980
Chicago/Turabian StyleWei, Yanlin, Xiaofeng Li, Xin Pan, and Lei Li. 2020. "Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms" Sensors 20, no. 23: 6980. https://doi.org/10.3390/s20236980
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