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.