aspg-18-1629-1792025591 20261015005311 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2021 2 2 Ahmed Sleem Ministry of communication and information technology, Egypt Ibrahim Elhenawy Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt Water scarcity is a significant issue in agriculture, making efficient irrigation practices crucial for sustainable farming. Integration of Internet of Things (IoT) and machine learning technologies are becoming of great importance to improve irrigation efficiency and reduce water usage. In this paper, we propose an intelligent irrigation system that take the advantage of IoT to improve the predictive analytics of groundwater levels. Our system used a deep learning to estimate the groundwater level using convolutional recurrent model that analyzed the sensory measurements necessary to predict groundwater levels. The model is trained on a large dataset of time series records and corresponding groundwater levels, allowing it to learn the complex patterns and relationships between time series features and groundwater levels. The experimental predictive analytics provided accurate irrigation recommendations, and the remote monitoring capabilities allowed farmers to adjust the irrigation schedule as needed. 2021 77 83 1629 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.020204 https://americaspg.com/journal/18/article/1629 https://americaspg.com/storage/31678874271.pdf