Automation in drip irrigation system: A comprehensive review with mathematical modeling and optimization algorithms

Jeet Chand (), Rupesh Acharya (), Roshan Pandey (), Milan Paudel () and Sanjeeb Bimali ()

International Journal of Sustainable Agricultural Research, 2025, vol. 12, issue 1, 67-80

Abstract: This study aims to review and document literature related to drip irrigation and its advancement towards automation in agriculture so that farmers can benefit from the optimum use of input resources, primarily water and fertilizer. Additionally, this review examines technological improvements such as sensor integration, wireless connectivity, and systems because the Internet of Things, microcontrollers, artificial intelligence, and real-time monitoring are critical tools for enhancing agricultural productivity and environmental sustainability. Mathematical models and formulations related to intelligent drip systems were reviewed, along with a deeper exploration of optimization algorithms employed, especially in terms of improving irrigation efficiency, resource optimization, and system performance. Furthermore, a critical analysis was undertaken in a comprehensive explanation of the system design, including real-world applications, with clear mathematical formulations and optimization models. This study found that among different irrigation methods, an intelligent drip system has the highest application efficiency, distribution uniformity, better crop yields, and input resource savings. Also, this study postulates that drip automation allows for accurate water and nutrient distribution, reducing fertilizer runoff and environmental harm. In contrast, drip irrigation has been found to be characterized by higher capital costs and the need for skilled personnel to manage the system, which are, however, equalized by higher yields and savings of production inputs. Reviewed literature indicated that high-valued cash crops are most appropriate for drip automation and suggest extensive application of automated drip systems for environmental sustainability in agriculture. This study recommends further research to make drip irrigation cost-effective, intelligent, and more farmer-friendly.

Keywords: Drip automation; Fertigation; Internet of Things; Mathematical modeling; Optimization algorithms; precision irrigation. (search for similar items in EconPapers)
Date: 2025
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