Reinforcement learning integrated with supervised learning for training of near infrared spectrum data for non-destructive testing of fruits (Conference Presentation)

Yuqi Li; Kulbir S. Ahluwalia; Simarjeet S. Saini

doi:10.1117/12.2557416

27 April 2020 Reinforcement learning integrated with supervised learning for training of near infrared spectrum data for non-destructive testing of fruits (Conference Presentation)

Yuqi Li, Kulbir S. Ahluwalia, Simarjeet S. Saini

Author Affiliations +

Proceedings Volume 11421, Sensing for Agriculture and Food Quality and Safety XII; 114210J (2020) https://doi.org/10.1117/12.2557416
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

A machine learning algorithm combining reinforcement learning and supervised learning is demonstrated for training of near infrared spectroscopy data for non-destructive measurement of fruit quality. The model optimizes the combination of pretreatment methods, discriminant methods and calibration methods and also the parameters used in the methods to achieve highest prediction correlations. The model achieves better results than manual combinations of the previously demonstrated models.

Conference Presentation

Citation Download Citation

Yuqi Li, Kulbir S. Ahluwalia, and Simarjeet S. Saini "Reinforcement learning integrated with supervised learning for training of near infrared spectrum data for non-destructive testing of fruits (Conference Presentation)", Proc. SPIE 11421, Sensing for Agriculture and Food Quality and Safety XII, 114210J (27 April 2020); https://doi.org/10.1117/12.2557416

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