The Development of a Prediction Model Related to Food Loss and Waste in Consumer Segments of Agrifood Chain Using Machine Learning Methods

Daniel Nijloveanu, Victor Tița, Nicolae Bold (), Doru Anastasiu Popescu, Dragoș Smedescu, Cosmina Smedescu and Gina Fîntîneru
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Daniel Nijloveanu: Faculty of Management and Rural Development, Slatina Branch, University of Agronomic Sciences and Veterinary Medicine Bucharest, 230088 Slatina, Romania
Victor Tița: Faculty of Management and Rural Development, Slatina Branch, University of Agronomic Sciences and Veterinary Medicine Bucharest, 230088 Slatina, Romania
Nicolae Bold: Faculty of Management and Rural Development, Slatina Branch, University of Agronomic Sciences and Veterinary Medicine Bucharest, 230088 Slatina, Romania
Doru Anastasiu Popescu: Department of Mathematics and Computer Science, Pitești University Center, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Romania
Dragoș Smedescu: Faculty of Management and Rural Development, University of Agronomic Sciences and Veterinary Medicine Bucharest, 011464 Bucharest, Romania
Cosmina Smedescu: Faculty of Management and Rural Development, University of Agronomic Sciences and Veterinary Medicine Bucharest, 011464 Bucharest, Romania
Gina Fîntîneru: Faculty of Management and Rural Development, University of Agronomic Sciences and Veterinary Medicine Bucharest, 011464 Bucharest, Romania

Agriculture, 2024, vol. 14, issue 10, 1-27

Abstract: Food loss and waste (FLW) is a primary focus topic related to all human activity. This phenomenon has a great deal of importance due to its effect on the economic and social aspects of human systems. The most integrated approach to food waste analysis is based on the study of FLW alongside the agrifood chain, which has also been performed in previous studies by the authors. This paper presents a modality of determination of food loss and waste effects with an emphasis on consumer segments in agrifood chains in the form of a predictive model based on statistical data collected based on specific methods in Romania. The determination is made comparatively, using two predictive machine learning-based methods and separate instruments (software), in order to establish the best model that fits the collected data structure. In this matter, a Decision Tree Approach (DTA) and a Neural Network Approach (NNA) will be developed, and common methodologies of the approaches will be applied. The results will determine predictive outcomes for a specific food waste (FW) agent (e.g., consumer) based on pattern recognition of the collected data. The results showed relatively high-accuracy predictions, especially for the NN approach, with lower performances using the DTA. The effects of the application of this predictive model will be expected to improve the food loss prevention measures within economic contexts when applied to real-life scenarios.

Keywords: food waste; food loss; predictive model; neural network; decision tree; economic behavior; agrifood chain; consumer (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1837-:d:1501829

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