The value of precision for image-based decision support in weed management

The value of precision for image-based decision support in weed management Decision support methodologies in precision agriculture should integrate the different dimensions composing the added complexity of operational decision problems. Special attention has to be given to the adequate knowledge extraction techniques for making sense of the collected data, processing the information for assessing decision makers and farmers in the efficient and sustainable management of the field. Focusing on weed management, the integration of operational aspects for weed spraying is an open challenge for modeling the farmers’ decision problem, identifying satisfactory solutions for the implementation of automatic weed recognition procedures. The objective of this paper is to develop a decision support methodology for detecting the undesired weed from aerial images, building an image-based viewpoint consisting in relevant operational knowledge for applying precision spraying. In this way, it is possible to assess the potential herbicide cost reductions of increased precision at the spraying device, selecting the appropriate weed precision spraying technology. Findings from this study indicate that the potential gains and marginal cost reductions of herbicides decrease significantly with increased precision in spraying. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

The value of precision for image-based decision support in weed management

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
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/s11119-017-9520-y
Publisher site
See Article on Publisher Site

Abstract

Decision support methodologies in precision agriculture should integrate the different dimensions composing the added complexity of operational decision problems. Special attention has to be given to the adequate knowledge extraction techniques for making sense of the collected data, processing the information for assessing decision makers and farmers in the efficient and sustainable management of the field. Focusing on weed management, the integration of operational aspects for weed spraying is an open challenge for modeling the farmers’ decision problem, identifying satisfactory solutions for the implementation of automatic weed recognition procedures. The objective of this paper is to develop a decision support methodology for detecting the undesired weed from aerial images, building an image-based viewpoint consisting in relevant operational knowledge for applying precision spraying. In this way, it is possible to assess the potential herbicide cost reductions of increased precision at the spraying device, selecting the appropriate weed precision spraying technology. Findings from this study indicate that the potential gains and marginal cost reductions of herbicides decrease significantly with increased precision in spraying.

Journal

Precision AgricultureSpringer Journals

Published: Mar 28, 2017

References

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