An Intelligent IoT-Based Irrigation Decision System Using Sensor Fusion and Long Short-Term Memory Networks
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Abstract
Efficient water management is a critical challenge in modern agriculture due to increasing water scarcity and climate variability. This paper presents an intelligent IoT-based irrigation decision system that integrates multi-sensor data fusion with Long Short-Term Memory (LSTM) networks for real-time irrigation scheduling. Environmental data, including soil moisture, temperature, humidity, and solar radiation, are collected through distributed IoT sensor networks and processed using data fusion techniques to improve data reliability and completeness. The fused data is then utilized by an LSTM-based deep learning model to capture temporal dependencies and accurately predict soil moisture trends and crop water requirements.
The proposed system enables dynamic and adaptive irrigation decisions by considering real-time environmental variations, thereby replacing traditional threshold-based methods. Experimental analysis demonstrates that the LSTM-based approach significantly improves prediction accuracy and water-use efficiency compared to conventional machine learning models. Furthermore, the system supports scalable deployment and remote monitoring through cloud integration, making it suitable for precision agriculture applications. Overall, the proposed framework enhances sustainable water management, optimizes irrigation scheduling, and contributes to the development of intelligent agricultural systems.
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