27 April 2020Reinforcement learning integrated with supervised learning for training of near infrared spectrum data for non-destructive testing of fruits (Conference Presentation)
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Yuqi Li,Kulbir S. Ahluwalia, andSimarjeet 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
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Yuqi Li, Kulbir S. Ahluwalia, 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