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All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00033 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00033
Number of page(s) 16
DOI https://doi.org/10.1051/e3sconf/202568000033
Published online 19 December 2025
  1. Food and Agriculture Organization. (2017). Water for sustainable food and agriculture. FAO. https://www.fao.org/3/a-i7959e.pdf [Google Scholar]
  2. A. El-Aabssi and A. Assir, “Modern Irrigation Systems Adoption in Morocco: Problems and Development Directions,” 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), (Fez, Morocco, 2025), pp. 1-6, doi: 10.1109/IRASET64571.2025.11007971 [Google Scholar]
  3. Ait Kadi, M., & Ziyad, A. (2018). Integrated Water Resources Management in Morocco. In World Water Council (Ed.), Global Water Security (pp. 143–163). Springer Singapore. http://dx.doi.org/10.1007/978-981-10-7913-9_6 [Google Scholar]
  4. Institut National de la Recherche Agronomique Morocco. (2023). État des lieux de l’agriculture protégée au Maroc. [Google Scholar]
  5. Morchid A, Et-Taibi B, Oughannou Z, Alami RE, Qjidaa H, Jamil MO, et al. IoT-Enabled Smart Agriculture for improving water management: a smart irrigation control using embedded systems and Server-Sent events. Scientific African 2024:e02527. https://doi.org/10.1016/j.sciaf.2024.e02527. [Google Scholar]
  6. Lachgar, N., Saikouk, H., Essabbar, M. et al. PRISMA-Guided Systematic Review on the Adoption of Artificial Intelligence and Embedded Systems for Smart Irrigation. Pure Appl. Geophys. 182, 2533–2582 (2025). https://doi.org/10.1007/s00024-025-03707-0 [Google Scholar]
  7. Mohammed B, Bekkay H, Migan-Dubois A, Adel M, Rabhi A. An intelligent irrigation system based on fuzzy logic control: A case study for Moroccan oriental climate region. 2nd international conference on Embedded Systems and Artificial Intelligence (ESAI’21), Apr 2021, Fez, Morocco.. https://hal.science/hal-03312289v1. [Google Scholar]
  8. Ahmed, B., Shabbir, H., Naqvi, S. R., & Peng, L. (2024). Smart agriculture: Current state, opportunities, and challenges. IEEE Access, 12, 144456-144478. https://doi.org/10.1109/ACCESS.2024.3471647 [Google Scholar]
  9. Lamane, H., Moussadek, R., Baghdad, B., Mouhir, L., Briak, H., Laghlimi, M., & Zouahri, A. (2022). Soil water erosion assessment in Morocco through modeling and fingerprinting applications: A review. Heliyon, 8(8), Article e10209. https://doi.org/10.1016/j.heliyon.2022.e10209 [Google Scholar]
  10. FAO. (2022). AQUASTAT Country Profile: Morocco. Food and Agriculture Organization. https://www.fao.org/aquastat/en/countries-and-basins/country-profiles/country/MAR [Google Scholar]
  11. Evett, S. R., O’Shaughnessy, S. A., Andrade, M. A., Kustas, W. P., Anderson, M. C., Schomberg, H. H., & Thompson, A. (2020). Precision Agriculture and Irrigation: Current U.S. Perspectives. Transactions of the ASABE, 63 (1), 57–67. https://doi.org/10.13031/trans.13355 [Google Scholar]
  12. World Bank. (2024). Promoting Climate Resilient Irrigation in Morocco. https://www.worldbank.org/en/news/feature/2024/06/17/promoting-climate-resilient-irrigation-in-morocco [Google Scholar]
  13. World Bank. (2018). Water Scarcity in Morocco: Analysis of Key Water Challenges. https://documents1.worldbank.org/curated/en/642681580455542456/pdf/Water-Scarcity-in-Morocco-Analysis-of-Key-Water-Challenges.pdf [Google Scholar]
  14. Ezzaeri K, Fatnassi H, Bouharroud R, Gourdo L, Bazgaou A, Wifaya A, et al. The effect of photovoltaic panels on the microclimate and on the tomato production under photovoltaic canarian greenhouses. Solar Energy 2018;173:1126–34. https://doi.org/10.1016/j.solener.2018.08.043. [Google Scholar]
  15. Hakimi F, Brech M. Opportunities and challenges of Peri-Urban agriculture on the fringes of the metropolis of Rabat, Morocco. International Journal of Food Science and Agriculture 2021;5:269–74. https://doi.org/10.26855/ijfsa.2021.06.009. [Google Scholar]
  16. Boubou Y, Fastner K, Buerkert A. Rural-urban transformation shapes oasis agriculture in Morocco’s High Atlas Mountains. Scientific Reports 2025;15. https://doi.org/10.1038/s41598-024-81569-7. [Google Scholar]
  17. Abou-Mehdi-Hassani, F., Zaguia, A., Ait Bouh, H. et al. Systematic literature review of smart greenhouse monitoring. SN COMPUT. SCI. 6, 95 (2025). https://doi.org/10.1007/s42979-024-03640-4 [Google Scholar]
  18. Food and Agriculture Organization (FAO). (2021a). “Greenhouse farming in arid regions: Economic and environmental benefits.” [Google Scholar]
  19. Anjum, M. N., Cheema, M. J. M., Hussain, F., & Wu, R.-S. (2023). Precision irrigation. In Precision Agriculture (pp. 85–101). Elsevier. https://doi.org/10.1016/B978-0-443-18953-1.00007-6 [Google Scholar]
  20. Food and Agriculture Organization (FAO). (2021b). “The State of Food and Agriculture in North Africa.” [Google Scholar]
  21. Yuan, G. N., Marquez, G. P. B., Deng, H., Iu, A., Fabella, M., Salonga, R. B., Ashardiono, F., & Cartagena, J. A. (2022). A review on urban agriculture: Technology, socio-economy, and policy. Heliyon, 8 (11). https://doi.org/10.1016/j.heliyon.2022.e11583 [Google Scholar]
  22. Benke, K., & Tomkins, B. (2017). Future food-production systems: Vertical farming and controlled-environment agriculture. Sustainability: Science, Practice and Policy, 13(1), 13–26. https://doi.org/10.1080/15487733.2017.1394054 [Google Scholar]
  23. Bastin J-F, Finegold Y, Garcia C, Gellie N, Lowe A, Mollicone D, et al. Response to Comments on “The global tree restoration potential.” Science (2019) ; 366. https://doi.org/10.1126/science.aay8108 [Google Scholar]
  24. Vidal, C. Y., Mangueira, J. R., Turini Farah, F., Rother, D. C., & Ribeiro Rodrigues, R. (2016). 8. Biodiversity Conservation of Forests and their Ecological Restoration in Highly-modified Landscapes. C. Gheler-Costa, M. C. Lyra-Jorge, & L. Martins Verdade (Eds.), (2016), Biodiversity in Agricultural Landscapes of Southeastern Brazil (pp. 136–150). De Gruyter Open. https://doi.org/10.1515/9783110480849-010 [Google Scholar]
  25. Gheler-Costa, Carla, Lyra-Jorge, Maria Carolina and Martins Verdade, Luciano. Biodiversity in Agricultural Landscapes of Southeastern Brazil, Warsaw, Poland: De Gruyter Open Poland, 2016. https://doi.org/10.1515/9783110480849 [Google Scholar]
  26. Allen RG. Chapter 5 - Introduction to crop evapotranspiration (ETc). In: Allen RG, Pereira LS, Raes D, Smith M, editors. Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome: Food and Agriculture Organization of the United Nations; 1998. [Google Scholar]
  27. Allen RG. Chapter 6 - ETc - Single crop coefficient (Kc). In: Allen RG, Pereira LS, Raes D, Smith M, editors. Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome: Food and Agriculture Organization of the United Nations; 1998. [Google Scholar]
  28. Togneri R, Santos DFD, Camponogara G, Nagano H, Custódio G, Prati R, et al. Soil moisture forecast for smart irrigation: The primetime for machine learning. (Expert Systems With Applications 2022). https://doi.org/10.1016/j.eswa.2022.117653 [Google Scholar]
  29. Sidhu RK, Kumar R, Rana PS. Machine learning based crop water demand forecasting using minimum climatological data. (Multimedia Tools and Applications 2020). https://doi.org/10.1007/s11042-019-08533-w [Google Scholar]
  30. Wei Z, Meng Y, Zhang W, Peng J, Meng L. Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau. (Remote Sensing of Environment 2019). https://doi.org/10.1016/j.rse.2019.02.022 [Google Scholar]
  31. Datta P, Faroughi SA. A multihead LSTM technique for prognostic prediction of soil moisture. (Geoderma 2023). https://doi.org/10.1016/j.geoderma.2023.116452 [Google Scholar]
  32. Cai H, Shi H, Liu S, Babovic V. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States. (Journal of Hydrology Regional Studies 2021). https://doi.org/10.1016/j.ejrh.2021.100930 [Google Scholar]
  33. Kurtulmuş E, Arslan B, Kurtulmuş F. Deep learning for proximal soil sensor development towards smart irrigation. (Expert Systems With Applications 2022). https://doi.org/10.1016/j.eswa.2022.116812 [Google Scholar]
  34. Alibabaei K, Gaspar PD, Assunção E, Alirezazadeh S, Lima TM. Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal. (Agricultural Water Management 2022). https://doi.org/10.1016/j.agwat.2022.107480 [Google Scholar]

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