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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. Abhishek Behl & Paridhi Rathi & V.V. Ajith Kumar, 2018. "Sustainability of the Indian auto rickshaw sector: identification of enablers and their interrelationship using TISM," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 31(2), pages 137-168.
    2. 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.
    3. Varinder Kumar Mittal & Rahul Sindhwani & Himanshu Shekhar & Punj Lata Singh, 2019. "Fuzzy AHP model for challenges to thermal power plant establishment in India," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 34(4), pages 562-581.
    4. Sood, Kirti & Singh, Simarjeet & Behl, Abhishek & Sindhwani, Rahul & Kaur, Sandeepa & Pereira, Vijay, 2023. "Identification and prioritization of the risks in the mass adoption of artificial intelligence-driven stable coins: The quest for optimal resource utilization," Resources Policy, Elsevier, vol. 81(C).
    5. 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.
    6. Michael L. Mann & James M. Warner & Arun S. Malik, 2019. "Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia," Climatic Change, Springer, vol. 154(1), pages 211-227, May.
    7. Ke Wang & Yafei Zhao & Rajan Kumar Gangadhari & Zhixing Li, 2021. "Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China," Sustainability, MDPI, vol. 13(19), pages 1-35, October.
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    1. Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025. "Macroeconomic and Labor Market Drivers of AI Adoption in Europe: A Machine Learning and Panel Data Approach," Economies, MDPI, vol. 13(8), pages 1-62, August.

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