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A Platform Approach to Smart Farm Information Processing

Author

Listed:
  • Mohammad Amiri-Zarandi

    (School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Mehdi Hazrati Fard

    (Department of Computer Science, University of Victoria, Victoria, BC V8W 2Y2, Canada)

  • Samira Yousefinaghani

    (School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Mitra Kaviani

    (School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Rozita Dara

    (School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada)

Abstract

With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance productivity in farming practices. In a smart farming scenario, large amounts of data are collected from diverse sources such as wireless sensor networks, network-connected weather stations, monitoring cameras, and smartphones. These data are valuable resources to be used in data-driven services and decision support systems (DSS) in farming applications. However, one of the major challenges with these large amounts of agriculture data is their immense diversity in terms of format and meaning. Moreover, the different services and technologies in a smart farming ecosystem have limited capability to work together due to the lack of standardized practices for data and system integration. These issues create a significant challenge in cooperative service provision, data and technology integration, and data-sharing practices. To address these issues, in this paper, we propose the platform approach, a design approach intended to guide building effective, reliable, and robust smart farming systems. The proposed platform approach considers six requirements for seamless integration, processing, and use of farm data. These requirements in a smart farming platform include interoperability, reliability, scalability, real-time data processing, end-to-end security and privacy, and standardized regulations and policies. A smart farming platform that considers these requirements leads to increased productivity, profitability, and performance of connected smart farms. In this paper, we aim at introducing the platform approach concept for smart farming and reviewing the requirements for this approach.

Suggested Citation

  • Mohammad Amiri-Zarandi & Mehdi Hazrati Fard & Samira Yousefinaghani & Mitra Kaviani & Rozita Dara, 2022. "A Platform Approach to Smart Farm Information Processing," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:838-:d:835951
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    References listed on IDEAS

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    Cited by:

    1. Martina Šestak & Daniel Copot, 2023. "Towards Trusted Data Sharing and Exchange in Agro-Food Supply Chains: Design Principles for Agricultural Data Spaces," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    2. Haoling Ren & Jiangdong Wu & Tianliang Lin & Yu Yao & Chang Liu, 2023. "Research on an Intelligent Agricultural Machinery Unmanned Driving System," Agriculture, MDPI, vol. 13(10), pages 1-19, September.
    3. Mohammad Amiri-Zarandi & Rozita A. Dara & Emily Duncan & Evan D. G. Fraser, 2022. "Big Data Privacy in Smart Farming: A Review," Sustainability, MDPI, vol. 14(15), pages 1-18, July.
    4. Jinying Li & Ananda Maiti & Jiangang Fei, 2023. "Features and Scope of Regulatory Technologies: Challenges and Opportunities with Industrial Internet of Things," Future Internet, MDPI, vol. 15(8), pages 1-27, July.
    5. Zhikai Ma & Kun Chong & Shiwei Ma & Weiqiang Fu & Yanxin Yin & Helong Yu & Chunjiang Zhao, 2022. "Control Strategy of Grain Truck Following Operation Considering Variable Loads and Control Delay," Agriculture, MDPI, vol. 12(10), pages 1-14, September.

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