A Systematic Review and Meta-Analysis of Intelligent Irrigation Systems

• Published in: IEEE access Vol. 12; pp. 128285 - 128304
• Main Authors: Hammouch, Hajar, El-Yacoubi, Mounim A., Qin, Huafeng, Berbia, Hassan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural sciences; Agriculture; Agriculture, economy and politics; Artificial Intelligence; Climate change; Computer Science; Global warming; Intelligent sensors; Internet of Things; Irrigation; Irrigation systems; Life Sciences; Management methods; Remote sensing; Remote sensors; Research projects; Reviews; Sensors; Smart agriculture; smart irrigation; Surveys; Systematics; Water conservation; Water management; Water resources; Remote Sensing; Smart irrigation; Water management; Internet of Things; Artificial Intelligence; Smart Agriculture
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3421322

The water crisis, global warming and climate changes have become recently prominent world issues. Saving and conserving water have become, therefore, an imperative for water resources' sustainability. In this context, dramatic innovations have come to light, e.g., Precision Agriculture. Numerous research projects on this subject have been conducted, as illustrated not only by the huge number of research works in this topic, but also by the dozens of surveys dedicated to. Surveys regarding smart agriculture are of two categories, surveys on smart agriculture in general in which smart irrigation is a subtopic among others, and surveys dedicated to smart irrigation. Existing surveys, however, suffer from limitations including low paper numbers, linear description without structural classification, and missing key information. To fill this gap, we propose, in this paper, a survey on SI that classifies the large body of literature into categories in a structured way. To this end, we have employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the inclusion or exclusion of articles. We have reviewed a total of 610 publications out of which 227 met the inclusion criteria. We have categorized the selected references into three conceptual clusters corresponding to: (1) 41.8% focusing on field measurements, based on IoT sensors, (2) 37% involving Remote Sensing (RS) and (3) 21.1% Artificial Intelligence (AI) methods. Each category is thoroughly described, including the inputs and outputs, along with a description of the various tasks employed in irrigation management. This systematic re-view is expected to serve as a useful reference for research on smart irrigation management methods.