Leveraging Data-Driven Machine Learning for Intelligent Shelf Life Prediction of Fresh Apple

Authors

Tharollo Mphahlele, University of the Western Cape, South Africa

Jean Frederic Isingizwe Nturambirwe, University of the Western Cape, South Africa

Ashwil Klein, University of the Western Cape, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.LGEP6576

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Substantial economic losses arise from changes that occur during postharvest handling of agro products, such as apples. As a result, there is a need for precise methods to monitor and estimate apple shelf life. This study compared conventional and machine learning models, such as MLR, RF, SVM, and FNN, using IoT sensors and LoRaWAN to monitor environmental factors such as temperature, humidity, VOCs, CO2, and O2. Laboratory analysis assessed key quality traits (background peel colour, texture, mass, size, total soluble solids, and titratable acidity). Results showed that FNN performed best (R² = 0.97859), with RF also performing strongly (R² = 0.97407) and having the lowest MAE. Both FNN and RF models demonstrated high accuracy, while MLR consistently had the highest errors, making FNN and RF the most effective for predicting apple shelf life.

Keywords

Shelf Life Prediction, Machine Learning, Fresh Apple, Modelling

Cite this paper

T. Mphahlele, J.F. Isingizwe Nturambirwe and A. Klein (2026) "Leveraging Data-Driven Machine Learning for Intelligent Shelf Life Prediction of Fresh Apple", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.LGEP6576

Access Paper

Click here to access this paper

IST-Africa Community Members can download papers by logging into their existing account.

If you wish to become a member of IST-Africa Community, please register your profile and you will then receive login details which you can use to access the papers.

Access to papers in the IST-Africa 2026 Conference Proceedings is for INDIVIDUAL USE only. Neither the Conference Proceedings (or individual double blind peer reviewed papers) are to be included in any public or institutional repository or processed in the context of any AI tool or large language model without prior written permission from the Publishers.

While the IST-Africa Institute and IIMC retain copyright of the Conference Proceedings, each author retains copyright to their individual paper. No paper or part thereof may be reproduced without the written permission of both the Conference Proceedings Publishers and appropriate author(s).