The Application of Artificial Intelligence Models for Food Security: A Review

Rebecca Sarku (), Ulfia A. Clemen and Thomas Clemen
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Rebecca Sarku: Multi-Agent Research and Simulation (MARS Group), Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany
Ulfia A. Clemen: Multi-Agent Research and Simulation (MARS Group), Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany
Thomas Clemen: Multi-Agent Research and Simulation (MARS Group), Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany

Agriculture, 2023, vol. 13, issue 10, 1-28

Abstract: Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.

Keywords: Artificial Intelligence; food security; machine learning; Global South; funding (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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