Gaussian Process Modeling of In-Season Physiological Parameters of Spring Wheat Based on Airborne Imagery from Two Hyperspectral Cameras and Apparent Soil Electrical Conductivity

Gaussian Process Modeling of In-Season Physiological Parameters of Spring Wheat Based on Airborne Imagery from Two Hyperspectral Cameras and Apparent Soil Electrical Conductivity

The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods i...

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Bibliographic Details
Main Authors: Wiktor R. Żelazny, Krzysztof Kusnierek, Jakob Geipel
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Remote Sensing
Subjects:
topdressing
imaging spectroscopy
Rikola HSI
Mjolnir V-1240
unmanned aerial vehicle
Bayesian machine learning
Online Access:https://www.mdpi.com/2072-4292/14/23/5977

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https://www.mdpi.com/2072-4292/14/23/5977

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