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Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen

Nature Food volume 2, pages 981–989 (2021)Cite this article

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Abstract

Overfertilization with nitrogen fertilizers has damaged the environment and health of soil, but standard laboratory testing of soil to determine the levels of nitrogen (mainly NH4+ and NO3−) is not performed regularly. Here we demonstrate that point-of-use measurements of NH4+, combined with soil conductivity, pH, easily accessible weather and timing data, allow instantaneous prediction of levels of NO3− in soil (R2 = 0.70) using a machine learning model. A long short-term memory recurrent neural network model can also be used to predict levels of NH4+ and NO3− up to 12 days into the future from a single measurement at day one, with \(R^2_{{{\mathrm{NH}}_4}^+} = 0.60\) and \(R^2_{{{\mathrm{NO}}_3}^-} = 0.70\), for unseen weather conditions. Our machine-learning-based approach eliminates the need for dedicated instruments to determine the levels of NO3− in soil. Nitrogenous soil nutrients can be determined and predicted with enough accuracy to forecast the impact of climate on fertilization planning and to tune timing for crop requirements, reducing overfertilization while improving crop yields.

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Fig. 1: Summary of the method.
Fig. 2: PoU soil ammonium sensor.
Fig. 3: Soil chemistry and nitrogen dynamics.
Fig. 4: ML model to predict soil nitrate.

Data availability

All data are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank EPSRC (grant no. EP/R010242/1), Innovate UK (grant no. 33486), Cytiva (formerly General Electric Healthcare Life Sciences) and Imperial College, Department of Bioengineering, for their generous support. F.G. and M.G. thank the Imperial College Centre for Processable Electronics and the Centre for Doctoral Training in Plastic Electronics, S. Yaliraki from Imperial College Department of Chemistry, T. Bell from Imperial College Department of Life Sciences, C. Mace from Tufts University Department of Chemistry and T. Schaul from (Google) DeepMind Technologies Ltd. F.G. also acknowledges Agri Futures Lab. M.K. acknowledges EPSRC DTP (grant no. 1846144). A.S.P.C. acknowledges BBSRC DTP (grant no. 2177734).

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Authors and Affiliations

  1. Department of Bioengineering, Imperial College London, London, UK

    Max Grell, Giandrin Barandun, Tarek Asfour, Michael Kasimatis, Alex Silva Pinto Collins, Jieni Wang & Firat Güder

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  1. Max Grell
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Contributions

M.G. designed and performed most of the experiments and wrote the manuscript. G.B. designed the homemade electronics. T.A. produced the supplementary videos and iterated on Fig. 1. M.K. performed the soil measurements and maintenance. A.S.P.C. and J.W. performed measurements with the homemade electronics. F.G. designed the experiments and edited the manuscript.

Corresponding author

Correspondence to Firat Güder.

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The authors declare no competing interests.

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Peer review information Nature Food thanks Arben Merkoçi, Anna Herland and Ramses Martinez for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–17.

Reporting Summary

Supplementary Video 1

Video illustration of the chemPEGS for measuring NH4+.

Supplementary Video 2

Video illustration of the ML models constructed to predict the levels of soil-N.

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Grell, M., Barandun, G., Asfour, T. et al. Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen. Nat Food 2, 981–989 (2021). https://doi.org/10.1038/s43016-021-00416-4

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