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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qinghe Zhao & Zifang Zhang & Yuchen Huang & Junlong Fang, 2022. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values," Agriculture, MDPI, vol. 12(9), pages 1-16, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1452-:d:913411
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    References listed on IDEAS

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    3. Lee, Tani & Tran, Anh & Hansen, James & Ash, Mark, 2016. "Major Factors Affecting Global Soybean and Products Trade Projections," Amber Waves:The Economics of Food, Farming, Natural Resources, and Rural America, United States Department of Agriculture, Economic Research Service, issue 04, pages 1-1, May.
    4. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
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    Cited by:

    1. Yourui Huang & Yuwen Liu & Tao Han & Shanyong Xu & Jiahao Fu, 2022. "Low Illumination Soybean Plant Reconstruction and Trait Perception," Agriculture, MDPI, vol. 12(12), pages 1-20, December.

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