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

Mengke Wang, Hongrui Zhang, Hongfei Lv, Chengye Liu, Jinhuan Xu and Xiangdong Li
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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

Czech Journal of Food Sciences, 2025, vol. 43, issue 1, 17-28

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.

Keywords: hyperspectral technology; machine learning; propagation filtering; spatial-spectral information (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:43:y:2025:i:1:id:109-2024-cjfs

DOI: 10.17221/109/2024-CJFS

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