HomeISTRADOR: Research Journal on Education, Technology and Innovationvol. 3 no. 2 (2025)

Assessment Of Multiparameter Soil Sensors For Measuring Temperature And Ph In Sandy, Loam, And Clay Soils Using Digital Meter As Reference

Jerome Jocosol

Discipline: environmental sciences

 

Abstract:

Soil temperature and pH strongly influences nutrient dynamics, microbial activity, and crop productivity, making accurate monitoring essential for precision agriculture. This study evaluated the accuracy, repeatability, and precision of multiparameter soil sensors in loam, sandy, and clay soils, using a digital meter as reference. Temperature accuracy was assessed through Mean Absolute Error (MAE) and Bland–Altman plots, while pH accuracy was analyzed using the same tools. Repeatability and precision were measured with Coefficient of Variation (CV) and Intraclass Correlation Coefficient (ICC). Results showed strong temperature accuracy, with MAE values of 0.87 °C (loam), 0.93 °C (sandy), and 0.90 °C (clay), all within the ±1 °C threshold. pH accuracy was moderate, with MAE values of 0.62, 0.98, and 1.04, reflecting overestimation in sandy and clay soils. Precision was acceptable (CV < 2.5% for temperature; 3–4% for pH), though ICC values (0.34–0.47) indicated moderate agreement. Multiparameter sensors are reliable for temperature but require calibration for pH.



References:

  1. Abate, G. G. (2024). Soil sampling and sample preparation. International Journal of Bioassays, 40(6), 1–5. https://doi.org/10.37532/0970-1907.24.40.6.1-5
  2. Em, S. (2024). Exploring experimental research: Methodologies, designs, and applications across disciplines. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4801767
  3. Giavarina, D. (2015). Understanding Bland–Altman analysis. Biochemia Medica, 25(2), 141–151. https://doi.org/10.11613/BM.2015.015
  4. Jalilibal, Z., Amiri, A., Castagliola, P., & Khoo, M. B. C. (2021). Monitoring the coefficient of variation: A literature review. Computers & Industrial Engineering, 162, 107600. https://doi.org/10.1016/j.cie.2021.107600
  5. Kelley, B., Ali, N., & Dong, Y. (2025). Methods to correct temperature-induced changes of soil moisture sensors to improve accuracy. MethodsX, 14, 103100. https://doi.org/10.1016/j.mex.2024.103100
  6. Knight, K. L. (2010). Study/experimental/research design: Much more than statistics. Journal of Athletic Training, 45(1), 98–100. https://doi.org/10.4085/1062-6050-45.1.98
  7. Liljequist, D., Elfving, B., & Roaldsen, K. S. (2019). Intraclass correlation – A discussion and demonstration of basic features. PLOS ONE, 14(7), e0219854. https://doi.org/10.1371/journal.pone.0219854
  8. Mosley, L. M., Rengasamy, P., & Fitzpatrick, R. (2024). Soil pH: Techniques, challenges and insights from a global dataset. European Journal of Soil Science. Advance online publication. https://doi.org/10.1111/ejss.70021
  9. Potdar, A., Fletcher, S., & Longstaff, A. P. (2016). Performance characterisation of a new photo-microsensor based sensing head for displacement measurement. Sensors and Actuators A: Physical, 238, 60–70. https://doi.org/10.1016/j.sna.2015.12.007.
  10. Wang, W., & Lu, Y. (2018). Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. IOP Conference Series: Materials Science and Engineering, 324(1), 012049. https://doi.org/10.1088/1757-899X/324/1/012049