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. Precision Agriculture Springer Journals

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

Loading next page...
Springer US
Copyright © 2010 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
Publisher site
See Article on Publisher Site


  • 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

You’re reading a free preview. Subscribe to read the entire article.

DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches


Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.



billed annually
Start Free Trial

14-day Free Trial