A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning
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...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00078.pdf |
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| author | Belarbi Zaid El Younoussi Yacine |
| author_facet | Belarbi Zaid El Younoussi Yacine |
| author_sort | Belarbi Zaid |
| collection | DOAJ |
| description | 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 ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies. |
| format | Article |
| id | doaj-art-94a2e3cdc256469fafed6969a3a41c1e |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-94a2e3cdc256469fafed6969a3a41c1e2025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010007810.1051/e3sconf/202560100078e3sconf_icegc2024_00078A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine LearningBelarbi Zaid0El Younoussi Yacine1Information System and Software Engineering Laboratory, Abdelmalek Essaadi UniversityInformation System and Software Engineering Laboratory, Abdelmalek Essaadi UniversityOptimizing 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 ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00078.pdf |
| spellingShingle | Belarbi Zaid El Younoussi Yacine A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning E3S Web of Conferences |
| title | A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning |
| title_full | A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning |
| title_fullStr | A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning |
| title_full_unstemmed | A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning |
| title_short | A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning |
| title_sort | review on optimizing water management in agriculture through smart irrigation systems and machine learning |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00078.pdf |
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