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Predicting Soil pH by Using Nearest Fields

  • Quoc Hung NgoEmail author
  • Nhien-An Le-Khac
  • Tahar Kechadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11927)

Abstract

In precision agriculture (PA), soil sampling and testing operation is prior to planting any new crop. It is an expensive operation since there are many soil characteristics to take into account. This paper gives an overview of soil characteristics and their relationships with crop yield and soil profiling. We propose an approach for predicting soil pH based on nearest neighbour fields. It implements spatial radius queries and various regression techniques in data mining. We use soil dataset containing about 4, 000 fields profiles to evaluate them and analyse their robustness. A comparative study indicates that LR, SVR, and GBRT techniques achieved high accuracy, with the \(R_2\) values of about 0.718 and \(MAE\) values of 0.29. The experimental results showed that the proposed approach is very promising and can contribute significantly to PA.

Keywords

Soil prediction Regression techniques Precision agriculture Data mining 

Notes

Acknowledgment

This work is part of CONSUS and is supported by the the SFI Strategic Partnerships Programme (16/SPP/3296) and is co-funded by Origin Enterprises Plc.

References

  1. 1.
    Aitkenhead, M.J., et al.: Prediction of soil characteristics and colour using data from the national soils inventory of Scotland. Geoderma 200, 99–107 (2013)CrossRefGoogle Scholar
  2. 2.
    da Chagas, C.S., et al.: Data mining methods applied to map soil units on tropical hillslopes in Rio de Janeiro, Brazil. Geoderma Reg. 9, 47–55 (2017)CrossRefGoogle Scholar
  3. 3.
    Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process.-Lett. Rev. 11(10), 203–224 (2007)Google Scholar
  4. 4.
    Bishop, T.F.A., McBratney, A.B.: A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma 103(1–2), 149–160 (2001)CrossRefGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Machine Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  6. 6.
    Han, P., Dong, D., et al.: A smartphone-based soil color sensor: for soil type classification. Comput. Electron. Agric. 123, 232–241 (2016)CrossRefGoogle Scholar
  7. 7.
    He, J., Li, H., et al.: Soil properties and crop yields after 11 years of no tillage farming in wheat-maize cropping system in north china plain. Soil Tillage Res. 113(1), 48–54 (2011)CrossRefGoogle Scholar
  8. 8.
    Ngo, Q.H., Le-Khac, N.-A., Kechadi, T.: Ontology based approach for precision agriculture. In: Kaenampornpan, M., Malaka, R., Nguyen, D.D., Schwind, N. (eds.) MIWAI 2018. LNCS (LNAI), vol. 11248, pp. 175–186. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03014-8_15CrossRefGoogle Scholar
  9. 9.
    Osman, K.T.: Soils: Principles, Properties and Management. Springer Science & Business Media, Dordrecht (2012).  https://doi.org/10.1007/978-94-007-5663-2CrossRefGoogle Scholar
  10. 10.
    Pietri, J.A., Brookes, P.: Relationships between soil pH and microbial properties in a UK arable soil. Soil Biol. Biochem. 40(7), 1856–1861 (2008)CrossRefGoogle Scholar
  11. 11.
    Shangguan, W., et al.: A China data set of soil properties for land surface modeling. J. Adv. Model. Earth Syst. 5(2), 212–224 (2013)CrossRefGoogle Scholar
  12. 12.
    Singh, P., et al.: Effects of sewage wastewater irrigation on soil properties, crop yield and environment. Agric. Water Manage. 103, 100–104 (2012)CrossRefGoogle Scholar
  13. 13.
    Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. Royal Stat. Soc.: Ser. B (Methodological) 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Waheed, T., et al.: Measuring performance in precision agriculture: cart a decision tree approach. Agric. Water Manage. 84(1–2), 173–185 (2006)CrossRefGoogle Scholar
  15. 15.
    Wang, F., et al.: Comparison of machine learning algorithms for soil salinity predictions in three dry land oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China. Eur. J. Remote Sens. 52(1), 256–276 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Quoc Hung Ngo
    • 1
    Email author
  • Nhien-An Le-Khac
    • 1
  • Tahar Kechadi
    • 1
  1. 1.University College DublinBelfieldIreland

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