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Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research

Author

Listed:
  • Konstantina Spanaki

    (Loughborough University)

  • Uthayasankar Sivarajah

    (University of Bradford)

  • Masoud Fakhimi

    (University of Surrey)

  • Stella Despoudi

    (University of Western Macedonia
    Aston University)

  • Zahir Irani

    (University of Bradford)

Abstract

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

Suggested Citation

  • Konstantina Spanaki & Uthayasankar Sivarajah & Masoud Fakhimi & Stella Despoudi & Zahir Irani, 2022. "Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research," Annals of Operations Research, Springer, vol. 308(1), pages 491-524, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03922-z
    DOI: 10.1007/s10479-020-03922-z
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    References listed on IDEAS

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    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Konstantina Spanaki & Zeynep Gürgüç & Richard Adams & Catherine Mulligan, 2018. "Data supply chain (DSC): research synthesis and future directions," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4447-4466, July.
    3. Mikalef, Patrick & Pateli, Adamantia, 2017. "Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA," Journal of Business Research, Elsevier, vol. 70(C), pages 1-16.
    4. Ilias O. Pappas & Patrick Mikalef & Michail N. Giannakos & John Krogstie & George Lekakos, 2018. "Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies," Information Systems and e-Business Management, Springer, vol. 16(3), pages 479-491, August.
    5. Elias G. Carayannis & Stelios Rozakis & Evangelos Grigoroudis, 2018. "Agri-science to agri-business: the technology transfer dimension," The Journal of Technology Transfer, Springer, vol. 43(4), pages 837-843, August.
    6. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
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    Cited by:

    1. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Tarun Jain & Jishnu Hazra & T. C. E. Cheng, 2023. "Analysis of upstream pricing regulation and contract structure in an agriculture supply chain," Annals of Operations Research, Springer, vol. 320(1), pages 85-122, January.
    3. Shengxing Yang, 2022. "A systematic literature review on the disruptions of artificial intelligence within the business world: in terms of the evolution of competences [Une revue systématique de la littérature sur les bo," Post-Print hal-03694170, HAL.
    4. Sushil Gupta & Hossein Rikhtehgar Berenji & Manish Shukla & Nagesh N. Murthy, 2023. "Opportunities in farming research from an operations management perspective," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1577-1596, June.

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