City Research Online

Abstract

Smart agriculture relies on efficient irrigation scheduling. Crop stress, nutrient leaching, and water loss are all caused by under or over-irrigation. Consequently, intelligent irrigation scheduling techniques will be critical to addressing the above issues shortly. Smart agriculture can be made more innovative and more efficient by using artificial intelligence (AI). The AI-centred approach carries enormous potential in estimating water requirements and the right time and place of irrigation. Motivated by the benefits of AI in irrigation scheduling, this article aims to provide a systematic review of AI-enabled irrigation scheduling techniques for intelligent agriculture. We have discussed various conventional irrigation scheduling techniques based on reference and crop evapotranspiration. Then, we present an in-depth analysis of the role of AI in designing and optimizing these irrigation scheduling techniques into AI-enabled intelligent irrigation scheduling techniques. Finally, various challenges and future research directions for designing and implementing AI-based irrigation scheduling techniques have been introduced.

Publication Type:	Article
Additional Information:	Copyright 2026 Sharma et al. Distributed under Creative Commons CC-BY 4.0.
Publisher Keywords:	AI, Irrigation scheduling, Evapotranspiration, Machine learning, Deep learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science S Agriculture > S Agriculture (General)
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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