Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this r...

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Bibliographic Details
Main Authors: Gurjinder S. Baath, Harpinder K. Baath, Prasanna H. Gowda, Johnson P. Thomas, Brian K. Northup, Srinivas C. Rao, Hardeep Singh
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sensors
Subjects:
partial least square
support vector machine
gaussian processes
soybean
pigeon pea
guar
tepary bean
Online Access:https://www.mdpi.com/1424-8220/20/3/867

Internet

https://www.mdpi.com/1424-8220/20/3/867

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