TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values

Qinghe Zhao, Zifang Zhang, Yuchen Huang and Junlong Fang ()
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Qinghe Zhao: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Zifang Zhang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Yuchen Huang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Junlong Fang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2022, vol. 12, issue 9, 1-16

Abstract: Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in four categories is collected. Ten features are selected using an extreme gradient boosting algorithm from 203 hyperspectral bands in a range of 400 to 1000 nm; a Gaussian radial basis kernel function support vector machine with optimization by the tree-structured Parzen estimator algorithm is built as the TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset, which is 9.786% higher for the vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model.

Keywords: hyperspectral technology; non-destructive testing; soybean; machine learning; support vector machine; extreme gradient boosting; tree-structured Parzen estimator (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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