| Titre : | Advancing smart irrigation systems with the Internet of Things and machine learning: a review for water-secure agricultural practices in water-scarce regions (2026) |
| Auteurs : | N.K. Mohammadi ; H. Mohammadi ; M.G. Arabzai ; M.U. Rahman ; Z.K. Wang |
| Type de document : | Article |
| Dans : | Engineering Agriculture (vol. 13, n. 5, October 2026) |
| Article en page(s) : | p. 26693 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 07 - ENVIRONNEMENT ; 7.3 - Eau. Gestion de l'EauThésaurus IAMM METHODE D'IRRIGATION ; UTILISATION DE L'EAU ; GESTION DES EAUX ; ASIE ; AFRIQUE ; MOYEN ORIENT |
| Résumé : | Efficient water resource management in agriculture is essential for ensuring food security and environmental sustainability, particularly in water-scarce regions. This review examines the integration of Internet of Things and machine learning technologies in the development of smart irrigation systems aimed at optimizing water use. By leveraging real-time data from soil moisture sensors, weather stations and crop-specific inputs, these systems enable precise irrigation scheduling and predictive decision-making. Internet-of-Things-based frameworks offer remote monitoring and control through mobile and cloud platforms, enhancing operational efficiency and crop yield. Machine learning algorithms, including supervised and deep learning models, further contribute by forecasting water requirements and detecting anomalies in irrigation patterns. Despite the promising benefits, such as reduced water waste, lower operational costs and improved crop productivity, significant challenges persist. These include high initial infrastructure costs, data integration issues, network limitations and concerns about data security. This review examines the integration of Internet of Things and machine learning technologies from case studies across Asia, Africa and the Middle East, highlighting both success stories and deployment barriers. Additionally, future prospects such as integration with renewable energy sources, blockchain for data transparency and low-cost solutions for smallholders are discussed. Efficient water resource management in agriculture is essential for ensuring food security and environmental sustainability, particularly in water-scarce regions. Ultimately, the convergence of the Internet of Things and machine learning in smart irrigation presents a transformative approach to achieving sustainable agriculture under the pressures of climate change and water scarcity. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.15302/J-FASE-2026693 |
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