Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Validation of soil moisture sensors in automated irrigation system and performance evaluation in maize

DOI
https://doi.org/10.14719/pst.6664
Submitted
11 December 2024
Published
25-06-2025
Versions

Abstract

A research work was conducted at the Agricultural College & Research Insti tute, Coimbatore, during the summer and winter seasons of 2023. It com prised two experiments. The first experiment evaluated five soil moisture sensors—Tensiometer, Time Domain Reflectometry (TDR), Theta Probe, Capacitance Sensor, and Watermark Sensor—against gravimetric soil mois ture measurements. The TDR sensor demonstrated the highest accuracy, with soil moisture readings closely matching gravimetric values at depths of 15 cm, 30 cm, and 45 cm. The second experiment assessed maize water re quirements under seven treatments: Tensiometer (T1), Soil Moisture Sensor (T2), Gravimetric Method (T3), Penman-Monteith Method (T4), Thornthwaite Method (T5), SEBAL Method (T6), and Conventional Method (T7). Results showed that sensor-based irrigation enhanced maize growth parameters, including plant height (214.8 cm and 217.8 cm), leaf number (16.4 and 16.2), dry matter production (17413 kg ha-1 and 17324 kg ha-1), and reduced time for tasseling (53.9 days and 53.2 days) and silking (61.2 days and 61.4 days), compared to conventional irrigation over the two seasons. Soil moisture sensor-based irrigation in maize achieved the highest water productivity (1.4 kg m-3 in summer and winter) and water use efficiency (13.6 and 14.0 kg ha-1 mm-1). The study concluded that sensor-based irrigation, particularly with TDR sensors, is effective for automating irrigation and improving water use efficiency. The accuracy of in-situ measurement using sensors and ten siometers surpassed the empirical Penman-Monteith and Thornthwaite Methods and satellite-derived SEBAL method, reinforcing the reliability of sensors in automated irrigation models resulting in water saving and in creased productivity. These findings significantly contribute to global water resource management by promoting efficient water use, reducing wastage, and addressing the challenges of water scarcity. Additionally, they advance precision agriculture by enabling site-specific irrigation practices, optimiz ing crop yield, and enhancing resource sustainability.

References

  1. 1. Dalin C, Wada Y, Kastner T, Puma MJ. Groundwater depletion embedded in international food trade. Nature. 2017;543(7647):700–04. https://doi.org/10.1038/nature21403
  2. 2. World Bank. India: Groundwater critical but diminishing. 2012.
  3. 3. Darouich HM, Pedras CMG, Gonçalves JM, Pereira LS. Drip vs. surface irrigation: A comparison focusing on water saving and economic returns using multicriteria analysis applied to cotton. Biosyst Eng. 2014;122:74–90. https://doi.org/10.1016/j.biosystemseng.2014.03.010
  4. 4. Ayars JE, Fulton A, Taylor B. Subsurface drip irrigation in California-Here to stay? Agric Water Manag. 2015;157:39–47. https://doi.org/10.1016/j.agwat.2015.01.001
  5. 5. Megersa G, Abdulahi J. Irrigation system in Israel: A review. Int J Water Resour Environ Eng. 2015;7(3):29–37. https://doi.org/10.5897/IJWREE2014.0556
  6. 6. Jaidka M, Bathla S, Kaur R. Improved technologies for higher maize production. In: IntechOpen, London, UK; 2019. https://doi.org/10.5772/intechopen.88997
  7. 7. Zhao W, Li J, Li Y, Yin J. Effects of drip system uniformity on yield and quality of Chinese cabbage heads. Agric Water Manag. 2012;110(1):118–28. https://doi.org/10.1016/j.agwat.2012.04.007
  8. 8. Getahun S, Kefale H, Gelaye Y. Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. Sci World J. 2024;2024(1):1–12. https://doi.org/10.1155/2024/
  9. 2126734
  10. 9. Gomez KA, Gomez AA. Statistical procedures for agricultural research. New York: John Wiley and Sons; 1984.
  11. 10. Zhou L, Yu D, Wang Z, Wang X. Soil water content estimation using high-frequency ground penetrating radar. Water. 2019;11(5):1–16. https://doi.org/10.3390/w11051036
  12. 11. Jones HG, Tardieu F, Granier C. Precision irrigation: Advances in soil moisture monitoring and irrigation scheduling. Irrig Sci. 2005;23(3):175–91.
  13. 12. Evett SR, Heng LK, Moutonnet P, Nguyen ML. Field estimation of soil water content: A practical guide to methods, instrumentation and sensor technology. Vienna: International Atomic Energy Agency (IAEA); 2008. p. 131–41.
  14. 13. Zhang Y, Liu L, Sun J, Li C, Wan Y, Ji Y. Application of time domain reflectometry to triaxial shear tests on hydrate-bearing sediments. Measurement. 2024;238:1–31. https://doi.org/10.1016/j.measurement.2024.115369
  15. 14. Abioye EA, Zainal AMS, Mahmud MSA, Buyamin S, Ishak MHI, Abd Rahman MKI. A review on monitoring and advanced control strategies for precision irrigation. Comput Electron Agric. 2020;173:105441. https://doi.org/10.1016
  16. /j.compag.2020.105441
  17. 15. El-Habbasha SF, Okasha EM, Abdelraouf RE, Mohammed ASH. Effect of pressured irrigation systems, deficit irrigation and fertigation rates on yield, quality and water use efficiency of groundnut. Int J ChemTech Res. 2014;15(7):1–11.
  18. 16. Durga C, Ramulu V, Umadevi M, Suresh K. Evaluation of the effectiveness of nanosensor (IITB) based irrigation system on water productivity and yield of maize (Zea mays). Int J Curr Microbiol Appl Sci. 2020;9(8):2697–706. https://doi.org/10.20546/ijcmas.2020.908.306
  19. 17. Perez M, Lopez D, Gonzalez A. Impact of water deficit stress in maize: Phenology and yield components. Agric Water Manag. 2020;242(3):1–11.
  20. 18. Soulis KX, Elmaloglou S, Dercas N. Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture-based drip irrigation scheduling systems. Agric Water Manag. 2015;148(2):258–68. https://doi.org/10.
  21. 1016/j.agwat.2014.10.015
  22. 19. Garofalo P, Rinaldi M. Modelling phenotypical traits to adapt durum wheat to climate change in a Mediterranean environment. Ital J Agrometeorol. 2013;2(1):25–38.
  23. 20. Parthasarathi T, Vanitha K, Mohandass S, Vered E. Evaluation of drip irrigation system for water productivity and yield of rice. Agron J. 2018;110(6):2378–89. https://doi.org/10.2134/agronj2018.01.0002
  24. 21. Gordon DAR, Coenders-Gerrits M, Sellers BA, Sadeghi SM, Van Stan JT II. Rainfall interception and redistribution
  25. by a common North American understory and pasture forb, Eupatorium capillifolium. Hydrol Earth Syst Sci Discuss. 2019;24(1):4587–99. https://doi.org/10.5194/hess-2019-579
  26. 22. Hokam EM, El‐Hendawy SE, Schmidhalter U. Drip irrigation frequency: The effects and their interaction with nitrogen fertilization on maize growth and nitrogen use efficiency under arid conditions. J Agron Crop Sci. 2011;197(3):186–201. https://doi.org/10.1111/j.1439-037X.2010.00460.x
  27. 23. Yadav PK, Choudhary S. Drip irrigation and mulches influence on performance of tomato (Lycopersicon esculentum) in arid Rajasthan. Prog Hortic. 2012;44(2):313–17.
  28. 24. Sidhu RK, Kumar R, Rana PS, Jat ML. Automation in drip irrigation for enhancing water use efficiency in cereal systems of South Asia: Status and prospects. Adv Agron. 2021;167(1):247–300. https://doi.org/10.1016/bs.agron.
  29. 2021.01.002
  30. 25. Kumar S, Yadav A, Kumar A, Hasanain M, Shankar K, Karan S. Climate-smart irrigation practices for improving water productivity in India: A comprehensive review. Int J Environ Clim Change. 2023;13(12):333–48. https://doi.org/10.9734/ijecc/2023/v13i123689
  31. 26. Fanish SA, Muthukrishnan P. Effect of drip fertigation and intercropping on growth, yield and water use efficiency of maize (Zea mays L.). Madras Agric J. 2011;98(7–9):1–6. https://doi.org/10.29321/MAJ.10.100285
  32. 27. Biswal P, Faisal A, Tripathy S, Swain DK, Jha MK, Mohan G. Influence of drip irrigation and straw mulching on economic feasibility and soil fertility of rice-potato system in subtropical India. Irrig Sci. 2024;1–14. https://doi.org/
  33. 10.1007/s00271-024-00951-5
  34. 28. Kumar JL, Imtiyaz M. Yield, irrigation production efficiency and economic returns of broccoli under variable drip irrigation and lateral spacing. J Sci Technol (Ghana). 2007;27(2):111–21. https://doi.org/10.4314/just.v27i2.33047
  35. 29. Mukherjee A, Kundu M, Sarkar S. Role of irrigation and mulch on yield, evapotranspiration rate and water use pattern of tomato (Lycopersicon esculentum L.). Agric Water Manag. 2010;98(1):182–89. https://doi.org/10.1016/j.
  36. agwat.2010.08.018
  37. 30. Sui R, Yan H. Field study of variable rate irrigation management in humid climates. Irrig Drain. 2017;66(3):327–39. https://doi.org/10.1002/ird.2111
  38. 31. Lathashree AV. Sensor-based irrigation management in rice (Oryza sativa L.) [M.Sc. thesis]. Bangalore: University of Agricultural Sciences GKVK; 2019.

Downloads

Download data is not yet available.