Using two public soil spectral libraries, we examined potential for low-cost nearinfrared devices for in-field testing. Results showed strong promise with conventional neural networks, comparable to previously published results with deep learning.

© 2020 The Author(s)

PDF Article
More Like This
Machine Learning Based Prediction of Motor Imagery and Motor Execution Tasks from Functional Near Infrared Spectroscopy Signals

Oğuzhan Aslan, Kurt Kağan Kurtoğlu, Kutay Yeşilalan, and Sinem Burcu Erdoğan
BM4C.2 Optics and the Brain (BRAIN) 2020

Performance Prediction of Established Lightpaths Using Machine Learning and Field Data

Christine Tremblay
C1F_2 Conference on Lasers and Electro-Optics/Pacific Rim (CLEOPR) 2020

Deep Learning Classification of Cartilage Integrity Using Near Infrared Spectroscopy

Isaac O Afara, Jaakko K Sarin, Simo Ojanen, Mikko Finnila, Walter Herzog, Simo Saarakkala, Rami K Korhonen, and Juha Töyräs
JTu3A.27 Clinical and Translational Biophotonics (Translational) 2018


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access OSA Member Subscription