NONE 20260715073201161 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Electrical Engineering and Computer Science 2769-2507 Moses Adeolu Agoi Lagos State University of Education Lagos NIGERIA https://orcid.org/0000-0002-8910-2876 Oluwadamilola Peace Agoi Federal University of Agriculture Abeokuta Ogun NIGERIA Samuel Olayiwola Ajaga Lagos State University of Education Lagos NIGERIA Oluwanifemi Opeyemi Agoi Obafemi Awolowo University Osun NIGERIA The targeted management of water resources in agricultural systems has been identified as the key approach of enhancing the sustainability, maximizing the water usage, and improving crop yields in the face of escalating stressors associated with the climate. In this paper, the author discusses how geo-spatial technologies (Geographic Information Systems (GIS), remote sensing and satellite data) can be integrated with Internet of Things (IoT) sensing networks and data analytics structures to make irrigation decisions at finer spatial and temporal scales. The combination allows real-time soil drought monitoring, adaptable to weather-driven irrigation controls, and mechanistic control systems that cut down on the use of water but still keep the crops healthy. The paper provides the state of the art of geo-spatial and IoT-based irrigation systems, measures performance in terms of water savings and farm productivity, and defines implementation frameworks, through a long review of recent academic and applied research. Early indications are that the combination of IoT and geospatial capabilities can save 30 percent of irrigation water consumption without loss of yield and can be used to perform predictive modelling and reactive scheduling that is far better than heuristic irrigation strategies. The main issues are still the standardization of data, the reliability of sensors, the connectivity of the network, and the introduction of machine learning models to optimize irrigation. Further requirements of AI-enhanced decision support models warranted by future research ought to involve field validation across a variety of agro-ecological settings. 07 15 2026 07 15 2026 115 9 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232027.2026.8.9 https://wseas.com/journals/eeacs/2026/a18eeacs-009(2026).pdf Abdelmoneim, A. A., Al Kalaany, C. M., Khadra, R., Derardja, B., & Dragonetti, G. (2025). Calibration of low-cost capacitive soil moisture sensors for irrigation management applications. Sensors, 25(2), 343. https://doi.org/10.3390/s25020343 Ahmad, A., Abbas, Z., Ahmed, M., & Abbas, S. (2020). Cropping patterns for phenology stability and resource conservation under extreme climates. Frontiers in Agronomy, 7, 1672935. https://doi.org/10.3389/fagro.2025.1672935 Al-Faraj, F. A., Al-Shehri, F. M., Al-Johani, A. M., & Scholz, M., Tigkas, D. (2021). Smart irrigation using weather forecast and IoT sensors. Agricultural Water Management, 256, 107014. https://doi.org/10.1016/j.agwat.2021.107014 Bwambale, E., Naangmenyele, Z., Iradukunda, P., & Agboka, K. M. (2022). Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies. Cogent Engineering, 9(1), 2100573. https://doi.org/10.1080/23311916.2022.2100 573 Chen, J., Zhang, L., & Wang, H. (2022). Multi-depth soil moisture sensing and irrigation optimization using IoT-based distributed nodes. Precision Agriculture, 23(4), 1120-1145. https://doi.org/10.1007/s11119-022-09880-w Dong, X., Werling, B., Cao, Z., & Li, G. (2024). Implementation of an in-field IoT system for precision irrigation management. Frontiers in Water, 6, 1353597. https://doi.org/10.3389/frwa.2024.1353597 Frontiers in Agriculture. (2026). IoT-based automatic irrigation systems for sustainable eggplant production: A comparative experimental study. Frontiers in Agriculture. Advance online publication. García, L., Parra, L., Jimenez, J. M., Lloret, J., & Mauri, P. V. (2023). IoT-based precision irrigation for horticultural crops: A comparative experimental study. Computers and Electronics in Agriculture, 204, 107560. https://doi.org/10.1016/j.compag.2022.10756 0 Mahmood, I., Zuo, W., et al. (2023). Mapping irrigated areas based on remotely sensed crop phenology and soil moisture. Agronomy, 13(6), 1556. https://doi.org/10.3390/agronomy13061556 Mohamed, Z. E., et al. (2026). IoT-driven smart irrigation system to improve water use efficiency. Scientific Reports, 16, 2609. https://www.nature.com/articles/s41598-025- 33826-6 Ochoa-Loza, F. J., Cruz-López, L., & Romero-Troncoso, R. J. (2024). IoTintegrated irrigation robotics for enhanced water use efficiency in smallholder farms. Journal of Cleaner Production, 388, 135920. https://doi.org/10.1016/j.jclepro.2023.13592 0 Patel, H., Singh, S. K., & Rana, S. K. (2023). Review of artificial intelligence and internet of things technologies in land and water management research during 1991–2021: A bibliometric analysis. Engineering Applications of Artificial Intelligence, 123, 106241. https://doi.org/10.1016/j.engappai.2023.1062 41 Reddy, V. R., & Swarnalatha, P. (2022). IoT and automation for efficient water management in agriculture. Journal of Precision Agriculture, 23(1), 67-85. https://doi.org/10.1007/s11119-021-09876-4 Raza, S. H., Ahmed, Z., Khan, H. Z., et al. (2021). A paradigm of GIS and remote sensing for crop water deficit assessment to improve irrigation distribution planning. Agricultural Water Management, 243, 106443. https://doi.org/10.1016/j.agwat.2020.106443 Saha, K., et al. (2025). Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. PubMed. https://pubmed.ncbi.nlm.nih.gov/40100906/ Samreen, T., Ahmad, M., Baig, M. T., Kanwal, S., & Nazir, M. Z. (2022). Remote sensing in precision agriculture for irrigation management. Environmental Sciences Proceedings, 23(1), 31. https://doi.org/10.3390/environsciproc20220 23031 Senthilkumar, P., Asha, V., Ramasubramanian, P., & Ramesh, G. (2022). IoT-based precision irrigation for agriculture. Research Articles. https://doi.org/10.53659/shareit.v2i4.43 Singh, R., Jan, F., & Min-Allah, N. (2023). Wireless sensor networks and IoT solutions for sustainable precision agriculture. IEEE Access, 11, 172756-172769. https://doi.org/10.1109/ACCESS.2020.3025 590 Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/rs12193136 Smart Irrigation Tech Overview. (2025). Integrating real-time sensing and cloud analytics for irrigation scheduling in vertisol soil conditions (Technical Report No. 2025-IT). Smart Irrigation Technology Group. Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart sensors and smart data for precision agriculture: A review. Sensors, 24(8), 2647. https://doi.org/10.3390/s24082647 Verma, N., & Rana, S. K. (2021). Intelligent and IoT-based applications for sustainable agriculture. In IoT and Decision Support Systems for Industry 4.0. Springer. https://doi.org/10.1007/978-3-030-77083- 9_12 Zia, U., Rehman, A., Harris, N., Fatima, S., & Khurram, M. (2023). Evaluating intelligent irrigation systems based on IoT in grain crops. Water (MDPI), 15(7), 1394. https://doi.org/10.3390/w15071394