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An Automatic Irrigation System Based on Hourly Cumulative Evapotranspiration for Reducing Agricultural Water Usage

Author

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  • Yongjae Lee

    (Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Seung-un Ha

    (Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Xin Wang

    (Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Seungyong Hahm

    (Department of Horticultural Science, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Kwangya Lee

    (Institute of Agricultural Science, Chungnam National University, Daejeon 34134, Republic of Korea)

  • Jongseok Park

    (Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Republic of Korea
    Department of Horticultural Science, Chungnam National University, Daejeon 34134, Republic of Korea)

Abstract

This study investigates the development and application of an automatic irrigation system based on hourly cumulative evapotranspiration (ET) to optimize cabbage growth while reducing agricultural water usage. Traditional irrigation methods often result in inefficient water use due to reliance on human judgment or fixed schedules. To address this issue, the proposed system utilizes environmental data collected from a field sensor (FS), the Korea meteorological administration (KMA), and a virtual sensor based on a machine learning model (ML) to calculate the hourly ET and automate irrigation. The ET was calculated using the FAO 56 Penman–Monteith (P-M) ET o . Experiments were conducted to compare the effectiveness of different irrigation levels, ranging from 40, 60, 80, and 100% crop evapotranspiration (ET c ), on plant growth and the irrigation water productivity (WP I ). During the 46-day experimental period, cabbage growth and WP I were higher in the FS and KMA 60% ET c levels compared to other irrigation levels, with water usage of 8.90 and 9.07 L/plant, respectively. In the ML treatment, cabbage growth and WP I were higher in the 80% ET c level compared to other irrigation levels, with water usage of 8.93 L/plant. These results demonstrated that irrigation amounts of approximately 9 L/plant provided the optimal balance between plant growth and water conservation over 46 days. This system presents a promising solution for improving crop yield while conserving water resources in agricultural environments.

Suggested Citation

  • Yongjae Lee & Seung-un Ha & Xin Wang & Seungyong Hahm & Kwangya Lee & Jongseok Park, 2025. "An Automatic Irrigation System Based on Hourly Cumulative Evapotranspiration for Reducing Agricultural Water Usage," Agriculture, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:308-:d:1580615
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    References listed on IDEAS

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    1. Liao, Renkuan & Zhang, Shirui & Zhang, Xin & Wang, Mingfei & Wu, Huarui & Zhangzhong, Lili, 2021. "Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept," Agricultural Water Management, Elsevier, vol. 245(C).
    2. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Green, Steve R. & Kirkham, M.B. & Clothier, Brent E., 2006. "Root uptake and transpiration: From measurements and models to sustainable irrigation," Agricultural Water Management, Elsevier, vol. 86(1-2), pages 165-176, November.
    4. Pereira, L.S. & Paredes, P. & Melton, F. & Johnson, L. & Mota, M. & Wang, T., 2021. "Prediction of crop coefficients from fraction of ground cover and height: Practical application to vegetable, field and fruit crops with focus on parameterization," Agricultural Water Management, Elsevier, vol. 252(C).
    5. Sixuan Xu & Kexin Li & Guanlin Li & Zhiyuan Hu & Jiaqi Zhang & Babar Iqbal & Daolin Du, 2022. "Canada Goldenrod Invasion Regulates the Effects of Soil Moisture on Soil Respiration," IJERPH, MDPI, vol. 19(23), pages 1-12, November.
    6. Sharma, Kiran & Irmak, Suat & Kukal, Meetpal S., 2021. "Propagation of soil moisture sensing uncertainty into estimation of total soil water, evapotranspiration and irrigation decision-making," Agricultural Water Management, Elsevier, vol. 243(C).
    7. Marin, Fábio R. & Angelocci, Luiz R. & Nassif, Daniel S.P. & Costa, Leandro G. & Vianna, Murilo S. & Carvalho, Kassio S., 2016. "Crop coefficient changes with reference evapotranspiration for highly canopy-atmosphere coupled crops," Agricultural Water Management, Elsevier, vol. 163(C), pages 139-145.
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