AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps

Guo, X., Axmann, H.B., Soethoudt, J.M., Kok, M.G. 2024. AI Monitoring Approach to Fill National Food Loss and Waste Data Gaps. Wageningen, The Netherlands: Wageningen University & Research.

The global challenge of reducing Food Loss and Waste (FLW) is critical to achieving the UN’s Sustainable Development Goals (SDGs), particularly the commitment to halving FLW by 2030. Despite widespread recognition of the environmental, economic, and social impacts of FLW, including the prominent greenhouse gas (GHG) emission issue related to climate change (Porter et al., 2016), quantifying it remains a persistent issue due to significant data gaps, especially at the national and sub-national levels. Many countries, particularly low- and middle-income countries (LMICs), struggle with the complexity of FLW monitoring due to limited resources, insufficient expertise in FLW data collection, and unclear data collection practices. These challenges hinder the identification of hotspot products and supply chain stages, the definition of strategic targets, and the design of effective interventions for reducing FLW (Axmann et al., 2024), therefore reducing the associated greenhouse gas (GHG) emissions.