A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning

Aug 4, 2026

Uploaded by Riley

Optimizing irrigation water usage is essential for sustainable agriculture amid increasing water scarcity and climate variability. Accurate evapotranspiration (ET) estimation underpins reliable crop water requirement calculations for irrigation decision-making. Conventional ET measurement techniques, including eddy-covariance systems and lysimeters, provide valuable data but are limited by cost and scalability. Recent machine learning advances enable more precise and efficient ET prediction and support smarter irrigation scheduling using real-time inputs from meteorological, soil, and remote sensing datasets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Water management in agriculture and irrigation needs

AI and machine learning in agriculture

Focus of the review: ET prediction and smart irrigation

Survey and literature context

A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning Zaid Belarbi1 Ύ͕ Yacine El Younoussi1 1 Information System and Software Engineering Laboratory, Abdelmalek Essaadi University, 93030 Tetouan, Morocco Abstract. Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in MLbased ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies. 1 Introduction Water plays a vital role in irrigation and agriculture worldwide. The Food and Agriculture Organization of the United Nations (FAO) reported in 2017 that 70% of freshwater withdrawn globally is used in agriculture to sustain the growing human population [1] . Future projections indicate that water demand for irrigated food production will double by 2050, increasing pressure on already limited freshwater supplies. Although the FAO anticipates only a 10% increase in agricultural water withdrawal by 2050 due to improved management Ύ Corresponding author: [zaid.belarbi@etu.uae.ac.ma](zaid.belarbi@etu.uae.ac.ma) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). and irrigation practices, efficient water use within the irrigation and agricultural sectors remains crucial to alleviate the strain on global water resources [2] . The application of Artificial Intelligence (AI), particularly Machine Learning (ML), is revolutionizing various sectors, including agriculture. AI technologies are being leveraged to address the pressing challenges of modern agriculture, such as resource optimization, yield improvement, and sustainability. According to [3], AI in agriculture can be broadly divided into four categories: water management, soil management, livestock management, and crop management (Fig. 1) . Soil management focuses on maintaining soil health and fertility. AI techniques are used to analyze soil properties, monitor nutrient levels, and provide precise recommendations for soil amendments, leading to improved crop yields and soil conservation. Livestock management includes monitoring the health and productivity of animals using AIdriven tools. Sensors and machine learning algorithms track animal behavior, detect diseases early, and optimize feeding strategies, contributing to better livestock welfare and productivity. Crop management encompasses a range of activities from sowing to harvesting, aimed at maximizing crop yields and quality. AI applications in crop management include disease detection, yield prediction, and precision farming techniques that optimize planting and

FAQs

Why is optimizing irrigation water usage critical for agriculture?

It supports sustainable agriculture as water scarcity and climate variability increase, and it helps reduce pressure on limited freshwater resources.

What role does evapotranspiration (ET) play in irrigation management?

ET is a key component for determining crop water requirements, making accurate ET estimation essential for effective irrigation scheduling.

How can machine learning improve ET estimation and smart irrigation?

Machine learning models can predict ET using meteorological, soil, and remote sensing data, and can automate irrigation schedules based on real-time inputs.

Aug 4, 2026

Uploaded by Riley