Prediction of protein content in malting barley using proximal and remote sensing

Prediction of protein content in malting barley using proximal and remote sensing This paper examines the prediction of within-field differences in protein in malting barley at a late growth stage using the Yara N-Sensor and prediction of its regional variation with medium resolution satellite images. Field predictions of protein in the crop at a late growth stage could be useful for harvest planning, whereas regional prediction of barley quality before harvest would be useful for the grain industry. The project was carried out in central Sweden where the variation in protein content of malting barley has been documented both within fields and regionally. Scanning with an N-sensor and crop sampling were carried out in 2007 and 2008 at several fields. The regional data used consisted of weather data, quality analyses of the malting barley delivered to the major farmers’ co-operative, crops grown and field boundaries. Satellite scenes (SPOT 5 and IRS-P6 LISS-III) were acquired from a date as close as possible to the N-sensor scans. Reasonable partial least squares (PLS) models could be constructed based on weather and reflectance data from either the N-sensor or satellite. The models used mainly reflectance data, but the weather data improved them. Better field models could be created with data from the N-sensor than from the satellite image, but a local satellite-based model based on a simple ratio (middle infrared/green) in combination with weather was useful in regional prediction of malting barley protein. A regional prediction model based only on the weather variables explained about half the variation in recorded protein. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Prediction of protein content in malting barley using proximal and remote sensing

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-010-9181-6
Publisher site
See Article on Publisher Site

Abstract

This paper examines the prediction of within-field differences in protein in malting barley at a late growth stage using the Yara N-Sensor and prediction of its regional variation with medium resolution satellite images. Field predictions of protein in the crop at a late growth stage could be useful for harvest planning, whereas regional prediction of barley quality before harvest would be useful for the grain industry. The project was carried out in central Sweden where the variation in protein content of malting barley has been documented both within fields and regionally. Scanning with an N-sensor and crop sampling were carried out in 2007 and 2008 at several fields. The regional data used consisted of weather data, quality analyses of the malting barley delivered to the major farmers’ co-operative, crops grown and field boundaries. Satellite scenes (SPOT 5 and IRS-P6 LISS-III) were acquired from a date as close as possible to the N-sensor scans. Reasonable partial least squares (PLS) models could be constructed based on weather and reflectance data from either the N-sensor or satellite. The models used mainly reflectance data, but the weather data improved them. Better field models could be created with data from the N-sensor than from the satellite image, but a local satellite-based model based on a simple ratio (middle infrared/green) in combination with weather was useful in regional prediction of malting barley protein. A regional prediction model based only on the weather variables explained about half the variation in recorded protein.

Journal

Precision AgricultureSpringer Journals

Published: Jun 20, 2010

References

  • Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
    Haboudane, D; Miller, JR; Trembley, N; Zarco-Tejada, PJ; Dextraze, L

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