Integration of IoT and Sensor Technologies for Sustainable Smart Irrigation Systems: Trends, Challenges, and Future Directions

Abstract

Efficient water management is a persistent challenge in modern agriculture, especially in arid and semi-arid regions. The development and adoption of advanced soil sensor technologies are essential for optimising water use and supporting sustainable, high-yield agricultural systems. This systematic review compares primary soil sensor methodologies and synthesises findings from 156 peer-reviewed studies published between 2015 and 2025. The review assesses dielectric techniques, including time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR), as well as capacitive sensors, tensiometers, neutron probes, and instruments for measuring salinity and pH. Meta-analytical results show that TDR systems achieve high accuracy (pooled RMSE: 0.016 m³/m³) but are associated with significant costs ($500–$1200 per unit). In contrast, low-cost capacitive sensors can provide acceptable accuracy (RMSE: 0.045 m³/m³) when rigorously calibrated. LoRaWAN (Long Range Wide Area Network) is identified as the most effective communication protocol for agricultural Internet of Things (IoT) applications, with transmission ranges of 2–15 km and battery lifespans of 2–10 years. The use of machine learning methods improves irrigation scheduling accuracy by 15–30%, and artificial intelligence (AI) systems can achieve water savings of 35–50%. Key research gaps include the absence of standardised calibration procedures, as 67% of studies lack comprehensive validation, limited long-term stability assessments, with only 23% reporting data beyond six months, and insufficient investigation of multi-sensor data fusion. The review concludes by identifying future research priorities to support the development of robust and scalable innovative irrigation systems for sustainable agriculture.