Leveraging Deep Learning and Cloud Computing for Water Usage Optimization in Agriculture: A Study

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Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Karan singh Pawar,

  • Swati Khanve,

  • Nitya Khare,

  1. Student, Sagar Institute of Research & Technology Excellence, Bhopal, Madhya Pradesh, India
  2. Professor, Sagar Institute of Research & Technology Excellence, Bhopal, Madhya Pradesh, India
  3. Professor, Sagar Institute of Research & Technology Excellence, Bhopal, Madhya Pradesh, India

Abstract

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Water scarcity and inefficient irrigation practices are significant challenges in modern agriculture. This study explores the integration of deep learning and cloud computing to optimize water usage in agricultural systems. Leveraging advancements in deep learning and cloud computing, researchers have developed innovative solutions for optimizing water usage. This review examines the state-of-the-art methodologies, technologies, and applications in smart irrigation systems. It explores how deep learning models and cloud platforms are transforming agricultural water management by enabling predictive analytics, real-time monitoring, and adaptive control mechanisms. Running deep learning models on the cloud can be energy-intensive. Future studies should explore energy-efficient AI models and sustainable cloud computing solutions. Access to high-quality, real-time agricultural data remains a challenge. Future research should focus on developing better data collection mechanisms and integrating more diverse datasets. The paper also highlights key challenges and future directions for integrating these technologies into large-scale agricultural practices.

Keywords: Deep Learning, Cloud Computing, Agriculture

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Karan singh Pawar, Swati Khanve, Nitya Khare. Leveraging Deep Learning and Cloud Computing for Water Usage Optimization in Agriculture: A Study. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-.
How to cite this URL:
Karan singh Pawar, Swati Khanve, Nitya Khare. Leveraging Deep Learning and Cloud Computing for Water Usage Optimization in Agriculture: A Study. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0



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References

  1. Waseem Hamdoon and Ahmet Zengin, “A Proposed Smart Irrigation Management System based on the IOT and Cloud Computing Technologies”, Middle East Journal of Science, 9(1): 42-56, 2023.
  2. A. Rabak, K. Uppuluri, F.F. Franco, N. Kumar, V.P. Georgiev, C. Gauchotte-Lindsay, C. Smith, R.A. Hogg, L. Manjakkal, Sensor system for precision agriculture smart watering can, Results in Engineering 19 (2023).
  3. M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, R. Kaliaperumal, Smart farming: internet of things (IoT)-Based sustainable agriculture, Agriculture (Switzerland) 12 (2022).
  4. A.A. Sadri, A.M. Rahmani, M. Saberikamarposhti, M. Hosseinzadeh, Data reduction in fog computing and internet of things: a systematic literature survey, Internet of Things (Netherlands) 20 (2022).
  5. E.T. Bouali, M.R. Abid, E.M. Boufounas, T.A. Hamed, D. Benhaddou, Renewable energy integration into cloud IoT-based smart agriculture, IEEE Access 10 (2022) 1175–1191.
  6. E.T. Bouali, M.R. Abid, E.M. Boufounas, T.A. Hamed, D. Benhaddou, Renewable energy integration into cloud IoT-based smart agriculture, IEEE Access 10 (2022) 1175–1191.
  7. O. Friha, “Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies,” IEEE CAA J. Autom. Sinica, vol. 8, p. 718–752, 2021.
  8. L. García, “IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture,” Sensors, vol. 20, p. 1042, 2020.
  9. N. K. Nawandar and V. R. Satpute, “IoT based low cost and intelligent module for smart irrigation system,” Computers and electronics in agriculture, vol. 162, p. 979–990, 2019.
  10. C. Kamienski, “Smart water management platform: IoT-based precision irrigation for agriculture,” Sensors, vol. 19, p. 276, 2019.
  11. A. del Pozo, N. Brunel-Saldias, A. Engler, S. Ortega-Farias, C. Acevedo-Opazo, G. A. Lobos, R. Jara-Rojas, M.A. Molina- Montenegro, Climate change impacts and adaptation strategies of agriculture in Mediterranean-climate regions (MCRs), Sustainability 11 (2019).
  12. J. Kwok and Y. Sun, “A smart IoT-based irrigation system with automated plant recognition using deep learning,” in Proceedings of the 10th international conference on computer modeling and Simulation, 2018.
  13. J. F. Velasco-Muñoz, “Sustainable irrigation in agriculture: An analysis of global research,” Water, vol. 11, p. 1758, 2019.
  14. A. Goap, “An IoT-based smart irrigation management system using Machine learning and open source technologies,” Computers and electronics in agriculture, vol. 155, p. 41–49, 2018.
  15. J. Kwok and Y. Sun, “A smart IoT-based irrigation system with automated plant recognition using deep learning,” in Proceedings of the 10th international conference on computer modeling and Simulation, 2018.
  16. M. Roopaei, P. Rad and K.-K. R. Choo, “Cloud of things in smart agriculture: Intelligent irrigation monitoring by thermal imaging,” IEEE Cloud computing, vol. 4, p. 10–15, 2017.
  17. F. Molle, O. Tanouti, Squaring the circle: agricultural intensification vs. water conservation in Morocco, Agric. Water Manag. 192 (2017) 170–179.
Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 28/03/2025
Accepted 14/04/2025
Published 01/05/2025
Publication Time 34 Days
171

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