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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

The value of precision for image-based decision support in weed management. / Franco de los Ríos, Camilo; Pedersen, Søren Marcus; Papaharalampos, Haris; Ørum, Jens Erik.

In: Precision Agriculture, Vol. 18, No. 3, 2017, p. 366–382.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Franco de los Ríos, C, Pedersen, SM, Papaharalampos, H & Ørum, JE 2017, 'The value of precision for image-based decision support in weed management', Precision Agriculture, vol. 18, no. 3, pp. 366–382. https://doi.org/10.1007/s11119-017-9520-y

APA

Franco de los Ríos, C., Pedersen, S. M., Papaharalampos, H., & Ørum, J. E. (2017). The value of precision for image-based decision support in weed management. Precision Agriculture, 18(3), 366–382. https://doi.org/10.1007/s11119-017-9520-y

Vancouver

Franco de los Ríos C, Pedersen SM, Papaharalampos H, Ørum JE. The value of precision for image-based decision support in weed management. Precision Agriculture. 2017;18(3):366–382. https://doi.org/10.1007/s11119-017-9520-y

Author

Franco de los Ríos, Camilo ; Pedersen, Søren Marcus ; Papaharalampos, Haris ; Ørum, Jens Erik. / The value of precision for image-based decision support in weed management. In: Precision Agriculture. 2017 ; Vol. 18, No. 3. pp. 366–382.

Bibtex

@article{df66605321154636bad162f025ab840a,
title = "The value of precision for image-based decision support in weed management",
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{\textquoteright} 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.",
author = "{Franco de los R{\'i}os}, Camilo and Pedersen, {S{\o}ren Marcus} and Haris Papaharalampos and {\O}rum, {Jens Erik}",
year = "2017",
doi = "10.1007/s11119-017-9520-y",
language = "English",
volume = "18",
pages = "366–382",
journal = "Precision Agriculture",
issn = "1385-2256",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - The value of precision for image-based decision support in weed management

AU - Franco de los Ríos, Camilo

AU - Pedersen, Søren Marcus

AU - Papaharalampos, Haris

AU - Ørum, Jens Erik

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1007/s11119-017-9520-y

DO - 10.1007/s11119-017-9520-y

M3 - Journal article

VL - 18

SP - 366

EP - 382

JO - Precision Agriculture

JF - Precision Agriculture

SN - 1385-2256

IS - 3

ER -

ID: 174694040