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

Yongjae Lee, Seung-un Ha, Xin Wang, Seungyong Hahm, Kwangya Lee and Jongseok Park ()
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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

Agriculture, 2025, vol. 15, issue 3, 1-18

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.

Keywords: precision agriculture; machine learning; environmental data; sustainable agriculture; irrigation water use efficiency (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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