Research Article
BibTex RIS Cite

A microcontroller - Based Irrigation Scheduling Using FAO Penman-Monteith Equation

Year 2023, , 15 - 25, 30.06.2023
https://doi.org/10.46592/turkager.1170630

Abstract

This study uses the Food and Agricultural Organization (FAO) Penman-Monteith equation to develop a crop water algorithm needed to automate the supply of specific amount of water to crops, depending on their different crop water requirements. This was done to deviate from the practice of supplying the same amount of water to different crops during irrigation practices which could lead to over-irrigation or under-irrigation resulting in pest infestation and eventually low yield. The crop water requirement for cocoyam, spinach and tomatoes were estimated using data from FAO. A microcontroller-based smart irrigation device incorporated with real-time clock was developed to supply the right amount of water to crops at the right time and duration daily. The implementation was done using a laboratory-scale irrigation test bed and experimental results reveal the effectiveness of the developed system in the automation of crop-specific irrigation systems and in line with their Crop Water Requirement (CWR). Possible applications include greenhouses where researchers have to apply a specific amount of water to crops for experiments; horticultural gardens and nurseries to mention a few.

Supporting Institution

NONE

Project Number

NONE

Thanks

NONE

References

  • Agugo BAC, Muoneke CO, Eno-Obongo EE, Asiegbu JE (2009). A Theoretical Estimate of Crop Evapotranspiration and Irrigation Water Requirements of Mungbean (Vigna Radiata) In a Low Land Rain Forest Location of Southeastern Nigeria. In Electronic Journal of Environmental, Agricultural and Food Chemistry, 8(9); p. 720-729.
  • AQUASTAT (2016). Water Uses. http://www.fao.org/nr/water/aquastat/water_use/index.stm, accessed 9th August, 2019
  • Dorji K, Dorji SD and Tshering P (2017). Irrigation Scheduling and Water Requirements for Citrus Mandarin (Citrus Reticulata Blanco) - A case study from Drujegang, Dagana Bhutan. Bhutan
  • Ewemoje TA, Fagbayide SD and Oluwasemire K.O (2018). Lysimeter Determination of Crop Coefficient of Drip Irrigated Jatropha Curcas. Federal University of Technology, Akure, Journal of Engineering and Engineering Technology. FUTAJEET Vol. 12 (1). p. 159 – 170.
  • Gangwar, A., Nayak, T. R., Singh, R. M., & Singh, A. (2017). Estimation of crop water requirement using CROPWAT 8.0 model for Bina command, Madhya Pradesh. Indian Journal of Ecology, 44, 71-76.
  • Kamienski, C., Soininen, J.P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Filev Maia, R., Torre Neto, A. (2019). Smart water management platform: Iot-based precision irrigation for agriculture. (19). p. 276.
  • Kizito M, Amini N and Taha SU (2016). Design and Implementation of a Smart Irrigation System for Improved Water-Energy Efficiency. Conference: 4th IET Clean Energy and Technology Conference (CEAT 2016). DOI: 10.1049/cp.2016.1357
  • Munoth P (2016). Sensor Based Irrigation: A Review. International Research Journal of Engineering and Technology (IRJET), NCACE conference proceedings, (p. 86). Jaipur, India.
  • Ogidan OK, Onile AE and Adegboro OG (2019). Smart Irrigation System: A Water Management Procedure. Agricultural Sciences, 10; p. 25-31. https://doi.org/10.4236/as.2019.101003
  • Ogidan OK., and Afia KR (2019). Smart irrigation system with an android-based remote logging and control. In IEEE Region 8 flagship conference (AFRICON), Gimpa Executive conference Centre, Accra, September, 25th – 27th 2019.
  • Omid A, Pedro F, Luis G and Zita V (2020). Agricultural irrigation scheduling for a crop management system considering water and energy use optimization, Energy Reports, 12(1). p. 133-139. https://doi.org/10.1016/j.egyr.2019.08.031.
  • Raeth PG (2020). Moving beyond manual software-supported precision irrigation to human-supervised adaptive automation. African Journal of Agricultural Research, 16(11), p. 1548-1553.
  • Rodriguez D, Reca J, Martinez J, Lopez-Luque R and Urrestarazu M (2015). Development of a New Control Algorithm for Automatic Irrigation Scheduling in Soilless Culture. Article in Applied Mathematics & Information Sciences 9(1); p. 47-56.
  • Sandeep K. and Deepali. Y (2017). A Survey on Automatic Irrigation System Using Wireless Sensor Network. International Journal of Current Engineering and Scientific Research. Volume-4, Issue 8
  • Surendran U, Sushanth CM, Mammen G and Joseph EJ (2017). FAO-CROPWAT model-based estimation of crop water need and appraisal of water resources for sustainable water resource management: Pilot study for Kollam district-humid tropical region of Kerala, India. Current Science (00113891), 112(1).
  • Torres-Sanchez R, Navarro-Hellin H, Guillamon-Frutos A, San-Segundo R, Ruiz-Abellón MC and Domingo-Miguel RA (2020). Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques. (12). p. 548
  • Wardlaw R and Bhaktikul K (2004). Applications of Genetic Algorithms for Irrigation Water Scheduling. Scotland. UK.
  • Yadav D, Awasthi MK. and Nema RK. (2018). Study on crop water requirement of field crops under different climatic conditions of Madhya Pradesh. Agricultural Science Digest, 38 (2).
  • Yogesh GG, Devendra SC and Hitendra CC (2016). A Review on Automated Irrigation System using Wireless Sensor Network. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 5, Issue 6.
  • Zia H, Rehman A, Harris NR, Fatima S and Khurram M (2021). An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm. Sensors. 21(12): p. 4175. https://doi.org/10.3390/s21124175
Year 2023, , 15 - 25, 30.06.2023
https://doi.org/10.46592/turkager.1170630

Abstract

Project Number

NONE

References

  • Agugo BAC, Muoneke CO, Eno-Obongo EE, Asiegbu JE (2009). A Theoretical Estimate of Crop Evapotranspiration and Irrigation Water Requirements of Mungbean (Vigna Radiata) In a Low Land Rain Forest Location of Southeastern Nigeria. In Electronic Journal of Environmental, Agricultural and Food Chemistry, 8(9); p. 720-729.
  • AQUASTAT (2016). Water Uses. http://www.fao.org/nr/water/aquastat/water_use/index.stm, accessed 9th August, 2019
  • Dorji K, Dorji SD and Tshering P (2017). Irrigation Scheduling and Water Requirements for Citrus Mandarin (Citrus Reticulata Blanco) - A case study from Drujegang, Dagana Bhutan. Bhutan
  • Ewemoje TA, Fagbayide SD and Oluwasemire K.O (2018). Lysimeter Determination of Crop Coefficient of Drip Irrigated Jatropha Curcas. Federal University of Technology, Akure, Journal of Engineering and Engineering Technology. FUTAJEET Vol. 12 (1). p. 159 – 170.
  • Gangwar, A., Nayak, T. R., Singh, R. M., & Singh, A. (2017). Estimation of crop water requirement using CROPWAT 8.0 model for Bina command, Madhya Pradesh. Indian Journal of Ecology, 44, 71-76.
  • Kamienski, C., Soininen, J.P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Filev Maia, R., Torre Neto, A. (2019). Smart water management platform: Iot-based precision irrigation for agriculture. (19). p. 276.
  • Kizito M, Amini N and Taha SU (2016). Design and Implementation of a Smart Irrigation System for Improved Water-Energy Efficiency. Conference: 4th IET Clean Energy and Technology Conference (CEAT 2016). DOI: 10.1049/cp.2016.1357
  • Munoth P (2016). Sensor Based Irrigation: A Review. International Research Journal of Engineering and Technology (IRJET), NCACE conference proceedings, (p. 86). Jaipur, India.
  • Ogidan OK, Onile AE and Adegboro OG (2019). Smart Irrigation System: A Water Management Procedure. Agricultural Sciences, 10; p. 25-31. https://doi.org/10.4236/as.2019.101003
  • Ogidan OK., and Afia KR (2019). Smart irrigation system with an android-based remote logging and control. In IEEE Region 8 flagship conference (AFRICON), Gimpa Executive conference Centre, Accra, September, 25th – 27th 2019.
  • Omid A, Pedro F, Luis G and Zita V (2020). Agricultural irrigation scheduling for a crop management system considering water and energy use optimization, Energy Reports, 12(1). p. 133-139. https://doi.org/10.1016/j.egyr.2019.08.031.
  • Raeth PG (2020). Moving beyond manual software-supported precision irrigation to human-supervised adaptive automation. African Journal of Agricultural Research, 16(11), p. 1548-1553.
  • Rodriguez D, Reca J, Martinez J, Lopez-Luque R and Urrestarazu M (2015). Development of a New Control Algorithm for Automatic Irrigation Scheduling in Soilless Culture. Article in Applied Mathematics & Information Sciences 9(1); p. 47-56.
  • Sandeep K. and Deepali. Y (2017). A Survey on Automatic Irrigation System Using Wireless Sensor Network. International Journal of Current Engineering and Scientific Research. Volume-4, Issue 8
  • Surendran U, Sushanth CM, Mammen G and Joseph EJ (2017). FAO-CROPWAT model-based estimation of crop water need and appraisal of water resources for sustainable water resource management: Pilot study for Kollam district-humid tropical region of Kerala, India. Current Science (00113891), 112(1).
  • Torres-Sanchez R, Navarro-Hellin H, Guillamon-Frutos A, San-Segundo R, Ruiz-Abellón MC and Domingo-Miguel RA (2020). Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques. (12). p. 548
  • Wardlaw R and Bhaktikul K (2004). Applications of Genetic Algorithms for Irrigation Water Scheduling. Scotland. UK.
  • Yadav D, Awasthi MK. and Nema RK. (2018). Study on crop water requirement of field crops under different climatic conditions of Madhya Pradesh. Agricultural Science Digest, 38 (2).
  • Yogesh GG, Devendra SC and Hitendra CC (2016). A Review on Automated Irrigation System using Wireless Sensor Network. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 5, Issue 6.
  • Zia H, Rehman A, Harris NR, Fatima S and Khurram M (2021). An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm. Sensors. 21(12): p. 4175. https://doi.org/10.3390/s21124175
There are 20 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering
Journal Section Research Articles
Authors

Olugbenga Kayode Ogidan 0000-0003-0639-2263

Samuel Dare Oluwagbayıde 0000-0002-8238-4321

Thomas Ale 0000-0002-9846-8319

Project Number NONE
Early Pub Date June 25, 2023
Publication Date June 30, 2023
Submission Date September 5, 2022
Acceptance Date February 10, 2023
Published in Issue Year 2023

Cite

APA Ogidan, O. K., Oluwagbayıde, S. D., & Ale, T. (2023). A microcontroller - Based Irrigation Scheduling Using FAO Penman-Monteith Equation. Turkish Journal of Agricultural Engineering Research, 4(1), 15-25. https://doi.org/10.46592/turkager.1170630

26831    32449  32450 32451 3245232453

International peer double-blind reviewed journal

The articles in the Turkish Journal of Agricultural Engineering Research are open access articles and the articles are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-NC-4.0)(https://creativecommons.org/licenses/by-nc/4.0/deed.en). This license allows third parties to share and adapt the content for non-commercial purposes with proper attribution to the original work. Please visit for more information this link https://creativecommons.org/licenses/by-nc/4.0/ 

Turkish Journal of Agricultural Engineering Research (TURKAGER) is indexed/abstracted in Information Matrix for the Analysis of Journals (MIAR), EBSCO, CABI, Food Science & Technology Abstracts (FSTA), CAS Source Index (CASSI).

Turkish Journal of Agricultural Engineering Research (TURKAGER) does not charge any application, publication, or subscription fees.

Publisher: Ebubekir ALTUNTAŞ

For articles citations to the articles of the Turkish Journal of Agricultural Engineering Research (TURKAGER), please click: