Food Security via Food Waste Prevention: Categorization of Household Food Waste for Artificial Intelligence-Driven Interventions †
1
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Leicestershire LE11 3TU, UK
2
Loughborough Business School, Loughborough University, Leicestershire LE11 3TU, UK
*
Author to whom correspondence should be addressed.
†
Presented at the International Conference on Industry 4.0 for Agri-Food Supply Chains: Addressing Socio-Economic and Environmental Challenges in Ukraine, Leicester, UK and Online, 24–25 July 2023.
Eng. Proc. 2023, 40(1), 4; https://doi.org/10.3390/engproc2023040004
Published: 14 July 2023
(This article belongs to the Proceedings of International Conference on Industry 4.0 for Agri-food Supply Chains: Addressing Socio-economic and Environmental Challenges in Ukraine)
Global food waste, around 1.3 billion tons yearly, presents significant implications, notably aggravating food insecurity for approximately 768 million people. Households contribute 61% of this waste, underlining the potential to increase food availability. Despite the significance of household food waste (HFW), the lack of standardized categorization poses challenges in addressing this issue effectively. This study establishes a comprehensive framework for understanding HFW within the food supply chain, aiming to enable targeted interventions to reduce specific waste categories. A systematic review of existing food waste and categorization literature is conducted to provide a background on the global food waste problem and the specific reasons why household food waste occurs. A framework that implements a nine-stage categorization process for HFW (see Figure 1) is described, resulting in 37 distinct food waste categories stemming from domestic environments. Results indicate that a significant proportion of HFW consists of edible and avoidable food waste, emphasizing the need for interventions that target these specific waste streams. By utilizing this framework, much-improved clarity about the types of food waste could be gained from consumer-focused studies. This improved data granularity would then enable far more targeted interventions to be conceived and developed to address the most prolific or problematic (e.g., highest cost or environmental impact) HFW streams. Due to increased data requirements, we proposed and discussed the suitability of this framework for implementation using intelligent systems. And the current research is discussed in the context of AI-driven customized food waste prevention capabilities. Ongoing work is addressing structured reasoning for household food waste according to household food management stages and the well-known COM-B model (capability, opportunity, motivation, behavior change). In conclusion, this study emphasizes the need for a standardized categorization to understand HFW and design effective data-driven, AI-assisted interventions based on the COM-B behavioral change model. By addressing the categorization challenges and offering practical recommendations, this study provides valuable insights for policymakers, researchers, and practitioners to reduce HFW, promote sustainable consumption practices, and increase global food security.
Author Contributions
Conceptualization, N.I.M.Y., E.W. and J.G.; methodology, N.I.M.Y. and E.W.; formal analysis, N.I.M.Y. and E.W.; resources, N.I.M.Y., E.W. and J.G.; data curation, N.I.M.Y. and E.W.; writing—original draft preparation, N.I.M.Y.; writing—review and editing, N.I.M.Y.; visualization, N.I.M.Y. and E.W.; supervision, E.W. and J.G.; project administration, N.I.M.Y., E.W.; funding acquisition, N.I.M.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Majlis Amanah Rakyat (REF NO: 330408389954).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
Figure 1.
Indicators to categorize food waste.
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MDPI and ACS Style
Yusoff, N.I.M.; Woolley, E.; Godsell, J. Food Security via Food Waste Prevention: Categorization of Household Food Waste for Artificial Intelligence-Driven Interventions. Eng. Proc. 2023, 40, 4. https://doi.org/10.3390/engproc2023040004
AMA Style
Yusoff NIM, Woolley E, Godsell J. Food Security via Food Waste Prevention: Categorization of Household Food Waste for Artificial Intelligence-Driven Interventions. Engineering Proceedings. 2023; 40(1):4. https://doi.org/10.3390/engproc2023040004
Chicago/Turabian StyleYusoff, Nurul Islam Mohamed, Elliot Woolley, and Jan Godsell. 2023. "Food Security via Food Waste Prevention: Categorization of Household Food Waste for Artificial Intelligence-Driven Interventions" Engineering Proceedings 40, no. 1: 4. https://doi.org/10.3390/engproc2023040004
