review · Sensors
Integrating the Internet of Things and machine learning into automated irrigation systems offers significant potential to improve agricultural water efficiency and crop yields. An evaluation of the entire management pipeline, spanning from environmental data collection to automated water application, demonstrates how these technologies link with broader precision agriculture methods. Successful implementation requires addressing critical operational factors, including system interoperability, industry standardisation, and cybersecurity protections. Gaps remain in connecting diverse sensor suites smoothly, though proposed technical solutions target fully autonomous and scalable operations. By optimising water distribution and supporting higher crop productivity, automated smart irrigation frameworks present viable pathways to addressing wider global food supply challenges.
Global agriculture faces mounting pressure to produce more food while conserving scarce water resources. Smart irrigation networks that combine real-time sensor data and machine learning can automatically apply the exact amount of water crops need. Understanding how to integrate, standardise, and secure these technologies helps practitioners build resilient farming systems that save water and sustain crop production.
This work serves agricultural technology developers, irrigation equipment manufacturers, and farm managers seeking fully autonomous water management systems. Because the study is a review evaluating current technologies, standardisation needs, and integration gaps across multi-sensor setups, the insights operate at an early-stage advisory level rather than presenting a deployable commercial product. Real-world adoption relies on industry overcoming identified hurdles in sensor integration, interoperability, and cybersecurity.
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This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.
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DOI: 10.3390/s24237480
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