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Analysis of challenges to implement artificial intelligence technologies in agriculture sector

Author

Listed:
  • Nitasha Hasteer

    (Amity University)

  • Archit Mallik

    (Amity University)

  • Deepesh Nigam

    (Amity University)

  • Rahul Sindhwani

    (Birla Institute of Management Technology)

  • Jean-Paul Belle

    (University of Cape Town)

Abstract

Artificial Intelligence (AI) plays a vital role in the agriculture sector. Its use in the agriculture industry to improve farming practices has increased over time. The uniqueness of AI in agriculture is its potential to transform conventional agricultural practices, opening the doors to greater productivity, sustainability, and, ultimately, a more secure global food supply. However, there are obstacles that limit the application of AI in this industry. Through a well-organized literature review, the study identified nine barriers that hinder the implementation of AI. To finalize the barriers for further investigation, the Delphi approach was employed. The barriers were analysed through modified total interpretive structural modelling (m-TISM) technique and categorized into 4 clusters using the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis. Lack of skilled workforce and extreme climatic conditions are major driving barriers that prevent effective AI adoption. Based on the findings, the study puts forward three propositions. Timely action on the recommendation can help mitigate the concerns and benefit the stakeholders in the agriculture sector.

Suggested Citation

  • Nitasha Hasteer & Archit Mallik & Deepesh Nigam & Rahul Sindhwani & Jean-Paul Belle, 2024. "Analysis of challenges to implement artificial intelligence technologies in agriculture sector," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(5), pages 1841-1860, May.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:5:d:10.1007_s13198-023-02164-z
    DOI: 10.1007/s13198-023-02164-z
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    References listed on IDEAS

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    1. Kumar, Abhishek & Sah, Bikash & Singh, Arvind R. & Deng, Yan & He, Xiangning & Kumar, Praveen & Bansal, R.C., 2017. "A review of multi criteria decision making (MCDM) towards sustainable renewable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 596-609.
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    3. Anshu Agrawal, 2020. "Modified Total Interpretive Structural Model of Corporate Financial Flexibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(4), pages 369-388, December.
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