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Smart Irrigation Systems Using IOT, Wireless Sensor Networks, and Machine Learning: A Review

Rucha Gaurwadkar1, Monica Shinde1, Pranoti Mote1, Vaishnavi Wakale1, Ashwini Khot1, Vinit Dalavi1

1Dr. D. Y. Patil Pratishthan’s College of Engineering Salokhenagar, Kolhapur, Maharashtra, 416007

Abstract - The increasing global demand for food combined with growing water scarcity has intensified the need for efficient irrigation management systems. Smart irrigation technologies, supported by the Internet of Things (IoT), Wireless Sensor Networks (WSN), and intelligent computational algorithms, are emerging as promising solutions for optimizing agricultural water use while maintaining crop productivity. This review analyzes findings from several representative studies that illustrate the evolution of irrigation technologies from traditional threshold-based control systems to advanced predictive and autonomous irrigation management frameworks. Significant developments include the implementation of microcontroller-based automation, distributed sensor networks for environmental monitoring, fuzzy logic–based decision models, and machine learning approaches such as Long Short-Term Memory (LSTM) networks. A comparative analysis of these approaches highlights differences in sensing infrastructure, communication protocols, and computational techniques. Furthermore, this review identifies several ongoing challenges, including the absence of standardized data frameworks, insufficient large-scale field validation, and computational limitations associated with deploying machine learning models on edge devices. Future research directions emphasize scalable architectures, integration of agro-climatological data, and the development of energy-efficient intelligent irrigation systems.

Key Words:  Long Short-Term Memory (LSTM), Internet of Things (IoT), Wireless Sensor Networks (WSN), Smart Irrigation.