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Smart Agriculture Cloud Using AI Based Techniques

Author

Listed:
  • Muhammad Junaid

    (Department of Information Technology, The University of Haripur, Haripur 22620, KPK, Pakistan)

  • Asadullah Shaikh

    (College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Mahmood Ul Hassan

    (College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Abdullah Alghamdi

    (College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Khairan Rajab

    (College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Mana Saleh Al Reshan

    (College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Monagi Alkinani

    (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21442, Saudi Arabia)

Abstract

This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

Suggested Citation

  • Muhammad Junaid & Asadullah Shaikh & Mahmood Ul Hassan & Abdullah Alghamdi & Khairan Rajab & Mana Saleh Al Reshan & Monagi Alkinani, 2021. "Smart Agriculture Cloud Using AI Based Techniques," Energies, MDPI, vol. 14(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5129-:d:617864
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    References listed on IDEAS

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    1. Sergej Svorobej & Patricia Takako Endo & Malika Bendechache & Christos Filelis-Papadopoulos & Konstantinos M. Giannoutakis & George A. Gravvanis & Dimitrios Tzovaras & James Byrne & Theo Lynn, 2019. "Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges," Future Internet, MDPI, vol. 11(3), pages 1-15, February.
    2. Anthony King, 2017. "Technology: The Future of Agriculture," Nature, Nature, vol. 544(7651), pages 21-23, April.
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    Cited by:

    1. Hu, Yang & House, Lisa A. & Gao, Zhifeng, 2022. "How do consumers respond to labels for crispr (gene-editing)?," Food Policy, Elsevier, vol. 112(C).
    2. Junchi Zhou & Wenwu Hu & Airu Zou & Shike Zhai & Tianyu Liu & Wenhan Yang & Ping Jiang, 2022. "Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    3. Wiranarongkorn, K. & Im-orb, K. & Patcharavorachot, Y. & Maréchal, F. & Arpornwichanop, A., 2023. "Comparative techno-economic and energy analyses of integrated biorefinery processes of furfural and 5-hydroxymethylfurfural from biomass residue," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    4. Liu, Suxia & Deichmann, Majken & Moro, Mariú A. & Andersen, Lars S. & Li, Fulin & Dalgaard, Tommy & McKnight, Ursula S., 2022. "Targeting sustainable greenhouse agriculture policies in China and Denmark: A comparative study," Land Use Policy, Elsevier, vol. 119(C).

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