Pearse, JonathanThawdar, YaminnSim, Sheau Lan (Alicia)Ooi, Melanie Po-LeenMcGuinness, Benjamin JohnReutemann, PeterFletcher, DaleDuke, Mike2025-05-142025-05-142024Pearse, J., Thawdar, Y., Sim, A., Ooi, M., Mcguinness, B., Reutemann, P., Fletcher, D., & Duke, M. (2024). Hyperspectral imaging and machine learning to identify epicuticular wax loss in Masena Blueberries for post-harvest freshness. IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering, 2793-2798. https://doi.org/10.1109/CASE59546.2024.107115902161-8070https://hdl.handle.net/10289/17372Blueberries harvested using mechanical harvesting techniques, like over-the-row (OTR) harvesters, are not suitable for the fresh market due to high damage rates. Assisted harvesting (AH) techniques using hand-held shakers offer a new approach which shows promise in increasing harvesting rates without compromising fruit quality. Epicuticular wax is one of the fruit qualities needing to be preserved during harvesting. This paper investigates the effectiveness of hyperspectral imaging techniques and machine learning to construct a model to identify epicuticular wax rapidly and non-destructively. The best performing model produced was a linear SVM, and had an F1-score of 98.6%. Additionally, this model was used to show that blueberries harvested using hand-held shakers retain more epicuticular wax than traditional hand-harvesting (HH) techniques, a result that shows promise of potentially increased fruit quality using automated methods.enThis is an accepted version of a conference paper presented at the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). © Copyright 2024 IEEE.Conference Contribution10.1109/CASE59546.2024.107115902161-808946 Information and Computing Sciences30 Agricultural, Veterinary and Food Sciences3008 Horticultural Production