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Classification of peanut variety based on hyperspectral imaging and improved extreme learning machine

Author

Listed:
  • Mengke Wang

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

  • Hongrui Zhang

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

  • Hongfei Lv

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

  • Chengye Liu

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

  • Jinhuan Xu

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

  • Xiangdong Li

    (Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China)

Abstract

Peanut as an important crop, plays an important role in agricultural production, which is rich in edible vegetable oil and protein. The variety of peanut affects the content of vegetable oil and protein. Therefore, the classification of peanut variety can better promote the sustainable development of agriculture. In this study, hyperspectral imaging technology is used to achieve peanut variety classification. In addition, the spatial-spectral extreme learning machine (SS-ELM) is proposed to process the hyperspectral data to get the final classification label. To fully explore the spatial structure information of hyperspectral data, propagation filtering is integrated into the framework of extreme learning machine (ELM). The average accuracy of the improved ELM model on five varieties of peanuts dataset (Luhua 11, Dabaisha, Xiaobaisha, Fenghua, and Luohanguo 308) is 98.32%, which is higher than other classic models. The experimental results show that the improved ELM can classify peanut of different varieties by hyperspectral imaging.

Suggested Citation

  • Mengke Wang & Hongrui Zhang & Hongfei Lv & Chengye Liu & Jinhuan Xu & Xiangdong Li, 2025. "Classification of peanut variety based on hyperspectral imaging and improved extreme learning machine," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 43(1), pages 17-28.
    • Handle: RePEc:caa:jnlcjf:v:43:y:2025:i:1:id:109-2024-cjfs
    DOI: 10.17221/109/2024-CJFS
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