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

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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.

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Acknowledgements

This research has been partially supported by the Innovation Fund Denmark (InnovationsFonden), under the Future Cropping partnership. Authors would also like to thank Prof. Jesper Rasmussen, from University of Copenhagen, for making available the images used in this study.

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Authors and Affiliations

  1. Department of Food and Resource Economics, Faculty of Science, University of Copenhagen, Rolighedsvej 25, 1958, Frederiksberg, Denmark

    Camilo Franco, Søren Marcus Pedersen, Haris Papaharalampos & Jens Erik Ørum

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  1. Camilo Franco
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  2. Søren Marcus Pedersen
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  3. Haris Papaharalampos
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  4. Jens Erik Ørum
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Correspondence to Camilo Franco.

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Franco, C., Pedersen, S.M., Papaharalampos, H. et al. The value of precision for image-based decision support in weed management. Precision Agric 18, 366–382 (2017). https://doi.org/10.1007/s11119-017-9520-y

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