IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i6p1237-d1170102.html
   My bibliography  Save this article

Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning

Author

Listed:
  • Yiping Peng

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    These authors contributed equally to this work.)

  • Ting Wang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Dongguan Institute of Surveying and Mapping, Dongguan 523000, China
    These authors contributed equally to this work.)

  • Shujuan Xie

    (Guangdong Academy of Social Sciences, Guangzhou 510635, China)

  • Zhenhua Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Chenjie Lin

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Yueming Hu

    (College of Tropical Crops, Hainan University, Haikou 570228, China)

  • Jianfang Wang

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Xiaoyun Mao

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

Abstract

Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca 2+ , K + , Mg 2+ , and Na + ) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg 2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca 2+ and Na + , and SPA was the optimal algorithm for determining the characteristic bands of soil K + and Mg 2+ . The most accurate estimation models for soil Ca 2+ , K + , Mg 2+ , and Na + contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg 2+ (R 2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca 2+ : R 2 = 0.83, RPIQ = 2.47; K + : R 2 = 0.83, RPIQ = 2.58; Na + : R 2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg 2+ content with an R 2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg 2+ content at the regional scale.

Suggested Citation

  • Yiping Peng & Ting Wang & Shujuan Xie & Zhenhua Liu & Chenjie Lin & Yueming Hu & Jianfang Wang & Xiaoyun Mao, 2023. "Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning," Agriculture, MDPI, vol. 13(6), pages 1-12, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1237-:d:1170102
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/6/1237/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/6/1237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    3. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    4. Yvan Devaux & Lu Zhang & Andrew I. Lumley & Kanita Karaduzovic-Hadziabdic & Vincent Mooser & Simon Rousseau & Muhammad Shoaib & Venkata Satagopam & Muhamed Adilovic & Prashant Kumar Srivastava & Costa, 2024. "Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    6. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    8. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
    9. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    10. Giorgos Foutzopoulos & Nikolaos Pandis & Michail Tsagris, 2024. "Predicting Full Retirement Attainment of NBA Players," Working Papers 2403, University of Crete, Department of Economics.
    11. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    12. Schrader, Silja & Graham, Sonia & Campbell, Rebecca & Height, Kaitlyn & Hawkes, Gina, 2024. "Grower attitudes and practices toward area-wide management of cropping weeds in Australia," Land Use Policy, Elsevier, vol. 137(C).
    13. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    14. Cooray, Upul & Watt, Richard G. & Tsakos, Georgios & Heilmann, Anja & Hariyama, Masanori & Yamamoto, Takafumi & Kuruppuarachchige, Isuruni & Kondo, Katsunori & Osaka, Ken & Aida, Jun, 2021. "Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis," Social Science & Medicine, Elsevier, vol. 291(C).
    15. Simon Besnard & Nuno Carvalhais & M Altaf Arain & Andrew Black & Benjamin Brede & Nina Buchmann & Jiquan Chen & Jan G P W Clevers & Loïc P Dutrieux & Fabian Gans & Martin Herold & Martin Jung & Yoshik, 2019. "Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    16. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
    17. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    18. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
    19. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    20. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1237-:d:1170102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.