Czech J. Food Sci., 2025, 43(1):17-28 | DOI: 10.17221/109/2024-CJFS

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

Mengke Wang, Hongrui Zhang, Hongfei Lv, Chengye Liu, Jinhuan Xu, Xiangdong Li
Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

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

Received: June 7, 2024; Revised: October 22, 2024; Accepted: December 16, 2024; Prepublished online: January 29, 2025; Published: February 28, 2025  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Wang M, Zhang H, Lv H, Liu C, Xu J, Li X. Classification of peanut variety based on hyperspectral imaging and improved extreme learning machine. Czech J. Food Sci. 2025;43(1):17-28. doi: 10.17221/109/2024-CJFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Carrin M.E., Carelli A.A. (2010): Peanut oil: Compositional data. European Journal of Lipid Science and Technology, 112: 697-707. Go to original source...
    2. Chang C.C., Lin C.J. (2011): LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2: 1-27. Go to original source...
    3. Chang J.H.R., Wang Y.C.F. (2015): Propagated image filtering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, June 7-12, 2015: 10-18. Go to original source...
    4. Deng G., Cahill L.W. (1993): An adaptive Gaussian filter for noise reduction and edge detection. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, USA, Oct 31-Nov 6, 1993: 1615-1619.
    5. Elad M. (2002): On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing, 11: 1141-1151. Go to original source... Go to PubMed...
    6. Elmasry G., Kamruzzaman M., Sun D.W., Allen P. (2012): Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review. Critical Reviews in Food Science and Nutrition, 52: 999-1023. Go to original source... Go to PubMed...
    7. Fabiyi S.D., Vu H., Tachtatzis C., Murray P., Harle D., Dao T.K., Andonovic I., Ren J., Marshall S. (2020): Varietal classification of rice seeds using RGB and hyperspectral images. IEEE Access, 8: 22493-22505. Go to original source...
    8. Gupta G. (2011): Algorithm for image processing using improved median filter and comparison of mean, median and improved median filter. International Journal of Soft Computing and Engineering, 1: 304-311.
    9. Huan L., Yaqian W., Xiaoming W., Dong A., Yaoguang W., Laixin L., Xing C., Yanlu Y. (2019): Study on detection method of wheat unsound kernel based on near-infrared hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 39: 223-229.
    10. Huang G.B., Zhu Q.Y., Siew C.K. (2006): Extreme learning machine: Theory and applications. Neurocomputing, 70: 489-501. Go to original source...
    11. Huang G.B., Zhou H., Ding X., Zhang R. (2011): Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42: 513-529. Go to original source... Go to PubMed...
    12. Huang G., Huang G.B., Song S., You K. (2015): Trends in extreme learning machines: A review. Neural Networks, 61: 32-48. Go to original source... Go to PubMed...
    13. Jiang H., Liu L., Chen Q. (2022): Rapid determination of acidity index of peanuts by near-infrared spectroscopy technology: Comparing the performance of different near-infrared spectral models. Infrared Physics and Technology, 125: 104308. Go to original source...
    14. Krishnapuram B., Carin L., Figueiredo M.A., Hartemink A.J. (2005): Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27: 957-968. Go to original source... Go to PubMed...
    15. Li J., Bioucas-Dias J.M., Plaza A. (2010): Exploiting spatial information in semi-supervised hyperspectral image segmentation. In: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland, June 14-16, 2010: 1-4. Go to original source...
    16. Liu Z., Jiang J., Qiao X., Qi X., Pan Y., Pan X. (2020): Using convolution neural network and hyperspectral image to identify moldy peanut kernels. LWT - Food Science and Technology, 132: 109815. Go to original source...
    17. Liu Q., Wang Z., Long Y., Zhang C., Fan S., Huang W. (2022): Variety classification of coated maize seeds based on Raman hyperspectral imaging. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 270: 120772. Go to original source... Go to PubMed...
    18. Mohi-Alden K., Omid M., Firouz M.S., Nasiri A. (2023): A machine vision-intelligent modelling based technique for in-line bell pepper sorting. Information Processing in Agriculture, 10: 491-503. Go to original source...
    19. Qu J.H., Liu D., Cheng J.H., Sun D.W., Ma J., Pu H., Zeng X.A. (2015): Applications of near-infrared spectroscopy in food safety evaluation and control: A review of recent research advances. Critical Reviews in Food Science and Nutrition, 55: 1939-1954. Go to original source... Go to PubMed...
    20. Sun L., Wu Z., Liu J., Xiao L., Wei Z. (2014): Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 53: 1490-1503. Go to original source...
    21. Wang L., Liu D., Pu H., Sun D.W., Gao W., Xiong Z. (2015): Use of hyperspectral imaging to discriminate the variety and quality of rice. Food Analytical Methods, 8: 515-523. Go to original source...
    22. Wright H. (1980): Commercial hybrid seed production. In: Fehr W.R., Hadley H.H. (eds): Hybridization of Crop Plants. Madison, American Society of Agronomy and Crop Science Society of America: 161-176. Go to original source...
    23. Wu Q., Xu L., Zou Z., Wang J., Zeng Q., Wang Q., Zhou M. (2022): Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models. Frontiers in Plant Science, 13: 1047479. Go to original source... Go to PubMed...
    24. Xuan L., Hong Z. (2017): An improved canny edge detection algorithm. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, Nov 24-26, 2017: 275-278. Go to original source...
    25. Yuan D., Jiang J., Qi X., Xie Z., Zhang G. (2020): Selecting key wavelengths of hyperspectral imagine for nondestructive classification of moldy peanuts using ensemble classifier. Infrared Physics and Technology, 111: 103518. Go to original source...
    26. Zhang L., Wang Y., Wei Y., An D. (2022): Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel. Food Chemistry, 370: 131047. Go to original source... Go to PubMed...
    27. Zou Z., Wang L., Chen J., Long T., Wu Q., Zhou M. (2022): Research on peanut variety classification based on hyperspectral image. Food Science and Technology, 42: e18522. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content