An Intelligent IoT-Based Irrigation Decision System Using Sensor Fusion and Long Short-Term Memory Networks
Article Sidebar
Main Article Content
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.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows others to share, copy, distribute, and adapt the work, provided that proper credit is given to the original author(s) and the source.
Authors retain copyright and grant Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) the right of first publication.
How to Cite
References
[1] K. Sumathy Kingslin and K. Vaishnavi, “A Comprehensive Survey on IoT-Based Smart Irrigation in Agriculture,” International Journal of Research and Scientific Innovation (IJRSI), Aug. 2025. doi: 10.51244/IJRSI.2025.120700071 DOI: https://doi.org/10.51244/IJRSI.2025.120700071
[2] P. K. Kashyap et al., “Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network,” IEEE Sensors Journal, vol. 21, no. 16, pp. 17479–17491, 2021. doi: 10.1109/JSEN.2021.3069266 DOI: https://doi.org/10.1109/JSEN.2021.3069266
[3] Rodrigo et al., “Data-Driven Water Need Estimation for IoT-Based Smart Irrigation: A Survey,” Expert Systems with Applications, vol. 225, Sept. 2023. doi: 10.1016/j.eswa.2023.120194 DOI: https://doi.org/10.1016/j.eswa.2023.120194
[4] Ahmed et al., “IoT Sensing for Advanced Irrigation Management: A Systematic Review of Trends, Challenges, and Future Prospects,” Sensors, 2025. doi: 10.3390/s25072291 DOI: https://doi.org/10.3390/s25072291
[5] J. Yu et al., “Deep Learning for Intelligent Irrigation Decision-Making: A Review,” Agricultural Water Management, vol. 320, Nov. 2025. doi: 10.1016/j.agwat.2025.109836 DOI: https://doi.org/10.1016/j.agwat.2025.109836
[6] Y. Saikai et al., “Deep Reinforcement Learning for Irrigation Scheduling Using High-Dimensional Sensor Feedback,” arXiv preprint, 2023. doi: 10.48550/arXiv.2301.00899 DOI: https://doi.org/10.1371/journal.pwat.0000169
[7] M. M. Rahaman et al., “Wireless Sensor Networks in Agriculture Through Machine Learning: A Survey,” Computers and Electronics in Agriculture, 2022. doi: 10.1016/j.compag.2022.106928 DOI: https://doi.org/10.1016/j.compag.2022.106928
[8] U. Roy B. P. et al., “A Smart Irrigation System Using IoT and Advanced Machine Learning Model,” Journal of Smart Internet of Things, 2024. doi: 10.2478/jsiot-2024-0009 DOI: https://doi.org/10.2478/jsiot-2024-0009
[9] O. Adeyemi et al., “Deep Learning-Based Soil Moisture Forecasting for Smart Irrigation,” Agricultural Water Management, vol. 256, 2021. doi: 10.1016/j.agwat.2021.107087 DOI: https://doi.org/10.1016/j.agwat.2021.107087
[10] W. Fang et al., “Evapotranspiration Estimation Using Deep Learning Models,” Computers and Electronics in Agriculture, vol. 174, 2020. doi: 10.1016/j.compag.2020.105448 DOI: https://doi.org/10.1016/j.compag.2020.105448
[11] Y. Zhang et al., “Stacked Autoencoder-Based Soil Moisture Prediction Using Wireless Sensor Networks,” Sensors, vol. 20, no. 21, 2020. doi: 10.3390/s20216411
[12] T. Kelly et al., “Reinforcement Learning for Adaptive Irrigation Control,” Computers and Electronics in Agriculture, vol. 162, pp. 536–545, 2019. doi: 10.1016/j.compag.2019.04.027 DOI: https://doi.org/10.1016/j.compag.2019.04.027
[13] X. Li et al., “Q-Learning-Based Irrigation Scheduling for Smart Agriculture,” Applied Artificial Intelligence, vol. 34, no. 11, pp. 822–838, 2020. doi: 10.1080/08839514.2020.1799458
[14] R. R. Shamshiri et al., “Advances in Smart Greenhouse Automation Using Artificial Intelligence,” Biosystems Engineering, vol. 189, pp. 93–114, 2020. doi: 10.1016/j.biosystemseng.2019.10.015 DOI: https://doi.org/10.1016/j.biosystemseng.2019.10.015
[15] O. Adeyemi et al., “Comparison of Machine Learning and Reinforcement Learning for Precision Irrigation,” Agricultural Systems, vol. 183, 2020. doi: 10.1016/j.agsy.2020.102889 DOI: https://doi.org/10.1016/j.agsy.2020.102889
[16] A. Anjum et al., “Cloud-Based IoT Smart Irrigation System Using Machine Learning,” IEEE Access, vol. 9, pp. 122325–122339, 2021. doi: 10.1109/ACCESS.2021.3109205
[17] L. García et al., “IoT-Based Smart Irrigation Systems: Recent Trends on Sensors and IoT Systems for Precision Agriculture,” Sensors, vol. 20, no. 4, 2020. doi: 10.3390/s20041042 DOI: https://doi.org/10.3390/s20041042
[18] O. Elijah et al., “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758–3773, 2018. doi: 10.1109/JIOT.2018.2844296 DOI: https://doi.org/10.1109/JIOT.2018.2844296
[19] H. M. Jawad et al., “Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review,” Sensors, vol. 17, no. 8, 2017. doi: 10.3390/s17081781 DOI: https://doi.org/10.3390/s17081781
[20] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015. doi: 10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539
[21] S. Hochreiter, “The Vanishing Gradient Problem During Learning Recurrent Neural Networks,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 2, pp. 107–116, 1998. doi: 10.1142/S0218488598000094 DOI: https://doi.org/10.1142/S0218488598000094
[22] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735
[23] V. Mnih et al., “Human-Level Control Through Deep Reinforcement Learning,” Nature, vol. 518, pp. 529–533, 2015. doi: 10.1038/nature14236 DOI: https://doi.org/10.1038/nature14236
[24] J. Liu et al., “Deep Reinforcement Learning for Irrigation Optimization: Advantages, Opportunities, and Challenges,” Computers and Electronics in Agriculture, 2025. doi: 10.1016/j.agwat.2025.110030 DOI: https://doi.org/10.1016/j.agwat.2025.110030
[25] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010. doi: 10.1016/j.comnet.2010.05.010 DOI: https://doi.org/10.1016/j.comnet.2010.05.010
[26] M. Sazzad et al., “IoT-Based Soil Moisture Measurement and Type Prediction Using Advanced Regression and Machine Learning Models,” Scientific Reports, vol. 15, 2025. doi: 10.1038/s41598-025-19444-2 DOI: https://doi.org/10.1038/s41598-025-19444-2