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Leveraging Machine Learning for Food Waste Reduction: An Analysis of Predictive Models
- 1 College of Food Science, Sichuan Agricultural University, China
- 2 Information School, University of Washington, USA
- 3 University of South Florida, Tampa, Florida, USA
* Author to whom correspondence should be addressed.
Abstract
Food waste remains a significant global issue with severe environmental, economic, and social impacts. Recent advancements in machine learning (ML) have shown promise in tackling this problem across various sectors, including household, catering, and hospitality industries. This study explores the application of machine learning models to predict and minimize food waste, focusing on key predictors such as household consumption patterns, retail demand, and food service estimates. By analyzing the effectiveness of random forests, this research highlights the ability of these models to enhance forecasting accuracy and waste management efficiency. Findings underscore the potential of ML to support sustainable food consumption, reduce waste generation, and achieve environmental benefits. This research offers insights into the practical deployment of ML for food waste mitigation, providing a roadmap for stakeholders aiming to adopt technology-driven waste reduction strategies.
Keywords
Food waste reduction; Machine learning; Predictive modeling; Random forest
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Cite this article
Yang,Y.;Zhao,C.;Lu,H. (2024). Leveraging Machine Learning for Food Waste Reduction: An Analysis of Predictive Models. Applied and Computational Engineering,112,154-160.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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