Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies Two soybean fields were monitored in 2001 and 2002 to determine the utility of multispectral imagery for locating and classifying crop anomalies. Crop anomalies may be due to planter problems, soil problems, weed infestations and stressed soybean plants. Three image collection dates per location for each year were used in a supervised classification analysis. In 2002, aerial images were evaluated for potential use as a directed scouting tool. Remotely sensed data as a scouting tool detected 50–100% of anomalies detected by ground truthing. The number of anomalies detected by aerial imagery decreased through the growing season, while the number of anomalies found from directed scouting remained relatively constant. Thus, agreement was higher later in the growing season, since remote sensing was detecting more anomalies than the ground truthing efforts did. Excluding bare soil and healthy soybean situations, anomalies due to stress on soybean plants in the form of iron chlorosis and stunted plants yielded highest classification accuracies, ranging from 83% to 90% both years. This is attributed to differences in coloration of soybean plants with iron chlorosis and lack of full canopy coverage of stunted soybean. Herbicide damage due to overlap of spray boom led to classification accuracies from 50% to 67%. The overlap of the spray boom was not widespread in the field; thus, fewer areas of interests could be constructed for testing purposes, which may explain the decrease in classification accuracies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Evaluating Remote Sensing for Determining and Classifying Soybean Anomalies

Loading next page...
Kluwer Academic Publishers
Copyright © 2005 by Springer Science+Business Media, Inc.
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


  • Spatial validation of crop models for precision agriculture
    Basso, B.; Ritchie, J. T.; Pierce, F. J.; Braga, R. P.; Jones, J. W.

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