Remote sensing applications for precision agriculture: A learning community approach

Remote sensing applications for precision agriculture: A learning community approach Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) may provide technologies needed for farmers to maximize the economic and environmental benefits of precision farming. However, most farmers do not have the skills to utilize these technologies effectively. Through a learning community approach led by the Upper Midwest Aerospace Consortium (UMAC), information was shared among scientists, agricultural producers, and data providers. Farmers and ranchers received value-added information derived from AVHRR, MODIS, ETM+, IKONOS, DigitInc's DALSA camera system and Positive Systems' ADAR 5500 digital aerial camera, over four growing seasons. Emphasis has been placed on reducing the time between data acquisition and delivery of value-added products to farmers, developing practical uses for the data and providing basic training so that the end users could understand how to interpret the information. Farmers and ranchers in rural areas were connected via wide-bandwidth satellite link to a central distribution center at the University of North Dakota. The farmers participated actively in evaluating the usefulness of inputs derived from remotely sensed data, sometimes even by conducting experiments on fertilizer and fungicide applications and assessing the economic benefits. Resulting applications included management zone delineation, verifying the effectiveness of variable-rate fertilizer applications, verifying the effectiveness of fungicide applications, quantifying the loss due to accidental spray drift damage, selecting acres within sugar beet fields under the Payment in Kind program, and monitoring physical damage due to insect, inundation, wind and hail. Several other in-field, early season management practices were also reviewed using high-resolution images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Remote sensing applications for precision agriculture: A learning community approach

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Publisher
Elsevier
Copyright
Copyright © 2003 Elsevier Inc.
ISSN
0034-4257
D.O.I.
10.1016/j.rse.2003.04.007
Publisher site
See Article on Publisher Site

Abstract

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) may provide technologies needed for farmers to maximize the economic and environmental benefits of precision farming. However, most farmers do not have the skills to utilize these technologies effectively. Through a learning community approach led by the Upper Midwest Aerospace Consortium (UMAC), information was shared among scientists, agricultural producers, and data providers. Farmers and ranchers received value-added information derived from AVHRR, MODIS, ETM+, IKONOS, DigitInc's DALSA camera system and Positive Systems' ADAR 5500 digital aerial camera, over four growing seasons. Emphasis has been placed on reducing the time between data acquisition and delivery of value-added products to farmers, developing practical uses for the data and providing basic training so that the end users could understand how to interpret the information. Farmers and ranchers in rural areas were connected via wide-bandwidth satellite link to a central distribution center at the University of North Dakota. The farmers participated actively in evaluating the usefulness of inputs derived from remotely sensed data, sometimes even by conducting experiments on fertilizer and fungicide applications and assessing the economic benefits. Resulting applications included management zone delineation, verifying the effectiveness of variable-rate fertilizer applications, verifying the effectiveness of fungicide applications, quantifying the loss due to accidental spray drift damage, selecting acres within sugar beet fields under the Payment in Kind program, and monitoring physical damage due to insect, inundation, wind and hail. Several other in-field, early season management practices were also reviewed using high-resolution images.

Journal

Remote Sensing of EnvironmentElsevier

Published: Nov 30, 2003

References

  • Remote Sensing of vegetation characteristics for farm management
    Jackson, R.D.
  • Opportunities and limitations for image-based remote sensing in precision crop management
    Moran, M.S.; Inoue, Y.; Barnes, E.M.

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