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Classification of Garlic ( Allium sativum L.) Crops by Fertilizer Differences Using Ground-Based Hyperspectral Imaging System

Author

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  • Hwanjo Chung

    (Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si 28644, Republic of Korea)

  • Seunghwan Wi

    (Vegetable Research Division, National Institute of Horticultural & Herbal Science, Wanju 55365, Republic of Korea)

  • Byoung-Kwan Cho

    (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, Republic of Korea)

  • Hoonsoo Lee

    (Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si 28644, Republic of Korea)

Abstract

In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques.

Suggested Citation

  • Hwanjo Chung & Seunghwan Wi & Byoung-Kwan Cho & Hoonsoo Lee, 2024. "Classification of Garlic ( Allium sativum L.) Crops by Fertilizer Differences Using Ground-Based Hyperspectral Imaging System," Agriculture, MDPI, vol. 14(8), pages 1-20, July.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1215-:d:1441821
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    References listed on IDEAS

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    1. Gago, J. & Douthe, C. & Coopman, R.E. & Gallego, P.P. & Ribas-Carbo, M. & Flexas, J. & Escalona, J. & Medrano, H., 2015. "UAVs challenge to assess water stress for sustainable agriculture," Agricultural Water Management, Elsevier, vol. 153(C), pages 9-19.
    2. Dorijan Radočaj & Ante Šiljeg & Rajko Marinović & Mladen Jurišić, 2023. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
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