Predicting Postharvest Food Losses at National and Sub-National Levels Using Data-Driven and Knowledge-Based Neural Networks

Xuezhen Guo (), Han Soethoudt, Melanie Kok and Heike Axmann
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Xuezhen Guo: Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WG Wageningen, The Netherlands
Han Soethoudt: Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WG Wageningen, The Netherlands
Melanie Kok: Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WG Wageningen, The Netherlands
Heike Axmann: Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WG Wageningen, The Netherlands

Sustainability, 2025, vol. 17, issue 10, 1-14

Abstract: Food loss is a major challenge for global food security, resource use efficiency, and sustainability. However, collecting primary food loss data is costly. This study explores a neural network-based approach to estimate food loss in the postharvest stage using the FAO’s food balance sheets for proof of concept. We investigated both traditional data-driven feedforward neural networks (FNNs) and knowledge-informed neural networks (KiNNs) using rice, wheat, and apple data from the FAO’s food balance sheets. The results show relatively high prediction accuracy with the data-driven approach when a larger amount of data is available. It also demonstrates the high potential of using KiNNs to improve the prediction accuracy when data availability is relatively limited. In general, the proposed approach shows great potential to be developed into an effective supplementary tool that can partially replace costly primary food loss data collection at the postharvest stage, which is particularly valuable when resources for primary data collection are limited.

Keywords: food losses; sustainable development; neural networks; knowledge-based AI; scientific machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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