Proposal Description

The session will feature a series of papers focused on the measurement and quantitative analysis of FLW along the food value chain. Researchers will explore various methodologies covering FW issues in households and food service in the hospitality sectors (eg. school canteens, and restaurants).
In the effort to reduce or prevent FLW, the integration of digital technologies has emerged as a pivotal and indispensable element (UNEP, 2021; Despoudi, 2021). Indeed, these technologies serve multiple purposes, with one of the primary objectives being monitoring and measurement (Trevisan and Formentini, 2023). For instance, they can be adapted to monitor food and environmental conditions, providing farmers with real-time data on factors such as temperature, moisture levels, and other relevant parameters (Banerjee et al., 2021).
We will demonstrate the potential of utilizing AI combined with machine learning and large language model technologies on machine vision and image recognition – a new perspective of using AI to track FW at the household level and predict waste quantity at the post-consumption stage, in comparison with the existing adoption of AI in the food service sector with waste tracking and food type detection (Tranfield et al., 2009), or at the operation and storage stages in the food production and retail sector (De Souza et al., 2021; Strotmann et al., 2022). We will present the real case studies and the primary data collected with FW trackers applied in school canteens and via chefs’ interviews in the working environment, which insert valuable insights with fresh perspectives. Furthermore, we will introduce innovative conceptual frameworks such as the noise indicator to assess the FW behaviour, FW measurement at the post-consumption stage, and the evaluation of the effectiveness of FW mitigation solutions. The papers presented in the session may be of help to researchers, including those working in official statistics, in defining an unambiguous framework for data collection, measurement and quantification of a topic central to the 2030 Agenda and sustainable development in each country.