Critical Survey of Deep Learning Irrigation scheduling review Paper
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Efficient irrigation scheduling is essential for sustainable agriculture, particularly in regions facing water scarcity and climate variability. Environmental Sensor Networks (ESNs), integrated with Internet of Things (IoT) technologies, enable continuous monitoring of soil moisture, temperature, humidity, rainfall, and other agro-meteorological parameters. The large volume and temporal complexity of such sensor data have motivated the adoption of deep learning techniques for intelligent irrigation decision-making.
This paper presents a critical survey of deep learning-driven irrigation scheduling systems that utilize environmental sensor networks. It reviews commonly used architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Deep Reinforcement Learning models, highlighting their applications in soil moisture forecasting, evapotranspiration estimation, crop water stress detection, and automated irrigation control. The survey compares cloud-based and edge-based deployment models and evaluates performance metrics, datasets, and feature engineering strategies used in recent studies.
Furthermore, this paper identifies key challenges including data heterogeneity, sensor noise, limited labeled datasets, energy constraints, and model generalization across diverse agricultural conditions. Finally, potential research directions such as explainable AI, transfer learning, multimodal data fusion, and adaptive reinforcement learning for autonomous irrigation optimization are discussed. This survey provides a comprehensive foundation for researchers and practitioners aiming to design intelligent, data-driven irrigation management systems.
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[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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.3390/s25072291 DOI: https://doi.org/10.3390/s25072291
[5] Jingxin Yu et al., “Deep learning for intelligent irrigation decision-making: A review,” Agricultural Water Management, vol. 320, Nov. 2025. https://doi.org/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. https://doi.org/10.48550/arXiv.2301.00899 DOI: https://doi.org/10.1371/journal.pwat.0000169
[7] Md Mohinur Rahaman et al., “Wireless sensor networks in agriculture through machine learning: A survey,” Computers and Electronics in Agriculture, 2022. https://doi.org/10.1016/j.compag.2022.106928 DOI: https://doi.org/10.1016/j.compag.2022.106928
[8] Upendra Roy B.P et al.,“A Smart Irrigation System Using the IoT and Advanced Machine Learning Model,” Journal of Smart Internet of Things, 2024. https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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, https://doi.org/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. https://doi.org/10.1109/ACCESS.2021.3109205
[17] L. García et al., “IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture,” Sensors, vol. 20, no. 4, 2020,. https://doi.org/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. https://doi.org/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, https://doi.org/10.3390/s17081781 DOI: https://doi.org/10.3390/s17081781
[20] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539
[21] S. Hochreiter, “The Vanishing Gradient Problem During Learning Recurrent Neural Nets,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 2, pp. 107–116, 1998. https://doi.org/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. https://doi.org/10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735
[23] Mnih, V., Kavukcuoglu, K., Silver, D. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015). https://doi.org/10.1038/nature14236 DOI: https://doi.org/10.1038/nature14236
[24] Jiamei Liu et al., “Deep Reinforcement Learning for irrigation optimization: Advantages, opportunities, and challenges,” Computers and Electronics in Agriculture, 2025. https://doi.org/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. https://doi.org/10.1016/j.comnet.2010.05.010 DOI: https://doi.org/10.1016/j.comnet.2010.05.010
[26] Sazzad, M., Ahmed, T., Kibria, G. et al. IoT based soil moisture measurement and type prediction using advanced regression and machine learning models. Sci Rep 15, 35730 (2025). https://doi.org/10.1038/s41598-025-19444-2 DOI: https://doi.org/10.1038/s41598-025-19444-2