Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data

Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data

Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning...

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Bibliographic Details
Main Authors: Chaya Smith, Senani Karunaratne, Pieter Badenhorst, Noel Cogan, German Spangenberg, Kevin Smith
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Remote Sensing
Subjects:
data mining
forage
high through-put phenotyping
near infrared spectroscopy
non-destructive sampling
predictive models
lolium perenne
Online Access:https://www.mdpi.com/2072-4292/12/6/928

Internet

https://www.mdpi.com/2072-4292/12/6/928

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