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<p>This paper introduces the&#xa0;development and explanation of a decision tree algorithm for irrigation scheduling. The proposed machine learning algorithms are designed to schedule irrigation in a predictive mode, in conjunction with a smart irrigation controller, namely IRIS, while considering the limited availability of climatic data in many locations. The&#xa0;empirical Abtew solar radiation equation is used to estimate potential evapotranspiration, and irrigation scheduling is calculated based on physical soil properties, crop characteristics, and specific features of the irrigation system. Irrigation frequency is readjusted in real-time by dividing the delivered irrigation water dose by crop evapotranspiration. IRIS, combined with a new algorithmic methodology, could be used as an innovative tool to optimize&#xa0;irrigation for open-field crops and landscapes.</p>

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IRIS: A Smart Irrigation Controller Based on Limited Climatic Data

  • 期刊论文

  • 发表日期：

    2025年7月6日

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  • Georgios Nikolaou,
  • Damianos Neocleous,
  • Antonio Manes,
  • Evangelini Kitta

摘要

This paper introduces the development and explanation of a decision tree algorithm for irrigation scheduling. The proposed machine learning algorithms are designed to schedule irrigation in a predictive mode, in conjunction with a smart irrigation controller, namely IRIS, while considering the limited availability of climatic data in many locations. The empirical Abtew solar radiation equation is used to estimate potential evapotranspiration, and irrigation scheduling is calculated based on physical soil properties, crop characteristics, and specific features of the irrigation system. Irrigation frequency is readjusted in real-time by dividing the delivered irrigation water dose by crop evapotranspiration. IRIS, combined with a new algorithmic methodology, could be used as an innovative tool to optimize irrigation for open-field crops and landscapes.