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

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2005 by Springer Science+Business Media, Inc.
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/s11119-005-3681-9
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Precision AgricultureSpringer Journals

Published: Aug 18, 2005

References

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

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