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Artificial Intelligence and Robotics for Reducing Waste in the Food Supply Chain: Systematic Literature Review, Theoretical Framework, and Research Agenda

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  • Sharma, Anjali

    (LIME Lab Low Proft LLC)

  • Singh, Param Vir
  • Musunur, Laxmi P.

Abstract

The COVID-19 pandemic has unraveled the inefficiencies in the global food supply chain. One glaring distortion is the wastage of close to a third of global food production, in the face of widespread food insecurity. With population explosion and climate change as additional pressure points, reducing food waste has emerged as an urgent imperative for achieving food security for all. In this paper, we develop a research framework and agenda for the use of Artificial Intelligence and robotics in reducing food loss and waste. The Cognitive Automation for Food (COGAF) has been developed as a theoretical framework for guiding future research. This framework delineates the research landscape into five distinct research streams: sensory enhancement, cognitive automation, physical automation, sensory-motor fusion, and collaborative automation. In order to develop a systematic research agenda, propositions have been developed in each of these research streams. In conjunction with the COGAF framework, this research agenda attempts to provide a road map for future research and knowledge creation pertaining to the use of AI and robotics to reduce food loss and waste.

Suggested Citation

  • Sharma, Anjali & Singh, Param Vir & Musunur, Laxmi P., 2020. "Artificial Intelligence and Robotics for Reducing Waste in the Food Supply Chain: Systematic Literature Review, Theoretical Framework, and Research Agenda," OSF Preprints h3jgb, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:h3jgb
    DOI: 10.31219/osf.io/h3jgb
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