Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Potential for Predicting Soil Properties Using Low Cost Near-Infrared Spectroscopy and Machine Learning

Not Accessible

Your library or personal account may give you access

Abstract

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 (CLEO/PR) 2020

Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques

Khouloud Abdelli, Danish Rafique, Helmut Grießer, and Stephan Pachnicke
Th2A.3 Optical Fiber Communication Conference (OFC) 2020

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.