Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market
Machine learning has been generally used for prediction and classification tasks in the food value chain. However, its application in the study of food waste has been limited. Therefore, this study explored the potential of predicting fruit waste at a wholesale level of the food value chain, using a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154325004338 |
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| author | Ikechukwu Kingsley Opara Douglas Chinenye Divine Yardjouma Silue Umezuruike Linus Opara Jude A. Okolie Olaniyi Amos Fawole |
| author_facet | Ikechukwu Kingsley Opara Douglas Chinenye Divine Yardjouma Silue Umezuruike Linus Opara Jude A. Okolie Olaniyi Amos Fawole |
| author_sort | Ikechukwu Kingsley Opara |
| collection | DOAJ |
| description | Machine learning has been generally used for prediction and classification tasks in the food value chain. However, its application in the study of food waste has been limited. Therefore, this study explored the potential of predicting fruit waste at a wholesale level of the food value chain, using a fresh produce wholesale market in South Africa as a case study. The study aimed to develop a machine learning model to predict fruit waste during marketing. Using historical data at the case study market from 2021 to 2023, different machine learning algorithms such as Random Forest, Gradient boosting, Decision tree, XGBoost, Extra tree and a Stacked Model were applied. The results revealed that fruits in the category of melons and citrus contributed more to fruit waste at the market, while the most waste was during spring and summer seasons, with the highest waste occurring in 2022. The decision tree and extra tree models were the most promising among the machine learning models in the training dataset, with an MAE of 112.19 each. At the same time, the XGBoost outperformed other models for the testing dataset with an MAE of 232.32. The study provided a solid baseline for future studies in this area and recommended integrating varied data for a more robust and accurate model. With further research and implementation, the developed machine learning model has the potential to aid market decisions and policymaking to reduce postharvest waste of fruits at the market, thereby enhancing profitability and sustainability. |
| format | Article |
| id | doaj-art-ed63f3a53dfe4db9ab95fe3cdfe7a893 |
| institution | DOAJ |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-ed63f3a53dfe4db9ab95fe3cdfe7a8932025-08-20T02:47:06ZengElsevierJournal of Agriculture and Food Research2666-15432025-08-012210206210.1016/j.jafr.2025.102062Application of machine learning in predicting fruit waste in a South African fresh produce wholesale marketIkechukwu Kingsley Opara0Douglas Chinenye Divine1Yardjouma Silue2Umezuruike Linus Opara3Jude A. Okolie4Olaniyi Amos Fawole5SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, 7600, South Africa; Department of Food Science, Stellenbosch University, Stellenbosch, 7600, South AfricaDepartment of Process Engineering and Energy Technology, Hochschule Bremerhaven, GermanySouth African Research Chairs Initiative in Sustainable Preservation and Agroprocessing Research, Postharvest and Agroprocessing Research, University of Johannesburg, Auckland Park, Johannesburg, 2006, South Africa; Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg, 2006, South AfricaSARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, 7600, South Africa; UNESCO International Centre for Biotechnology, Nsukka, 410001, Enugu State, NigeriaGallogly College of Engineering, University of Oklahoma, Norman, OK, 73019, USASouth African Research Chairs Initiative in Sustainable Preservation and Agroprocessing Research, Postharvest and Agroprocessing Research, University of Johannesburg, Auckland Park, Johannesburg, 2006, South Africa; Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg, 2006, South Africa; Corresponding author. South African Research Chairs Initiative in Sustainable Preservation and Agroprocessing Research, Postharvest and Agroprocessing Research, University of Johannesburg, Auckland Park, Johannesburg, 2006, South Africa.Machine learning has been generally used for prediction and classification tasks in the food value chain. However, its application in the study of food waste has been limited. Therefore, this study explored the potential of predicting fruit waste at a wholesale level of the food value chain, using a fresh produce wholesale market in South Africa as a case study. The study aimed to develop a machine learning model to predict fruit waste during marketing. Using historical data at the case study market from 2021 to 2023, different machine learning algorithms such as Random Forest, Gradient boosting, Decision tree, XGBoost, Extra tree and a Stacked Model were applied. The results revealed that fruits in the category of melons and citrus contributed more to fruit waste at the market, while the most waste was during spring and summer seasons, with the highest waste occurring in 2022. The decision tree and extra tree models were the most promising among the machine learning models in the training dataset, with an MAE of 112.19 each. At the same time, the XGBoost outperformed other models for the testing dataset with an MAE of 232.32. The study provided a solid baseline for future studies in this area and recommended integrating varied data for a more robust and accurate model. With further research and implementation, the developed machine learning model has the potential to aid market decisions and policymaking to reduce postharvest waste of fruits at the market, thereby enhancing profitability and sustainability.http://www.sciencedirect.com/science/article/pii/S2666154325004338Machine learningFruit wasteWholesale marketPredictionFood value chain |
| spellingShingle | Ikechukwu Kingsley Opara Douglas Chinenye Divine Yardjouma Silue Umezuruike Linus Opara Jude A. Okolie Olaniyi Amos Fawole Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market Journal of Agriculture and Food Research Machine learning Fruit waste Wholesale market Prediction Food value chain |
| title | Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market |
| title_full | Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market |
| title_fullStr | Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market |
| title_full_unstemmed | Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market |
| title_short | Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market |
| title_sort | application of machine learning in predicting fruit waste in a south african fresh produce wholesale market |
| topic | Machine learning Fruit waste Wholesale market Prediction Food value chain |
| url | http://www.sciencedirect.com/science/article/pii/S2666154325004338 |
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