MEASURING WHAT WE MISJUDGE: EXPLORING THE POTENTIAL AND LIMITATIONS OF AI FOR FOOD WASTE QUANTIFICATION

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• Available online in Detritus - Volume 36 - September 2026
• Pages 3-14

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Abstract

Food waste remains a critical yet insufficiently explored global challenge, and accurate quantification is essential for developing effective mitigation strategies. Traditional food waste measurement methods face multiple limitations, prompting growing interest in alternative approaches such as AI-based technologies. This exploratory study evaluates the potential of AI image analysis for food waste quantification under real-world conditions. The method involves comparing AI-estimated and human-estimated food waste weights against a benchmark of scale-measured weights to assess estimation accuracy and error patterns. Our findings indicate that while AI technologies show promise as scalable quantification tools, portion effect on estimation errors and variability in generalization across models underscores the need for thoughtful selection, and the performance could be substantially improved through further model training with larger and more diverse datasets. Overall, this study highlights both the potential and present limitations of AI-driven food waste quantification, emphasizing the need for continued research using more advanced models and expanded training data.

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Keywords

• Food waste quantification
• Food waste tracking
• Food recognition
• Artificial intelligence
• AI image analysis
• Large language model

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Editorial History

• Received: 06 Mar 2026
• Revised: 17 Sep 2026
• Accepted: 22 Sep 2026
• Available online: 30 Sep 2026

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