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RoboWeedSupport - Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems

Published online by Cambridge University Press:  01 June 2017

P. Rydahl ,
N.-P. Jensen ,
M. Dyrmann ,
P. H. Nielsen  and
R. N. Jørgensen
P. Rydahl*
Affiliation:
Department of AgroEcology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark & IPM Consult ApS, Hovedgaden 32, 4295 Stenlille, Denmark
N.-P. Jensen
Affiliation:
I∙GIS, Voldbjergvej 14A, 1. Sal, 8240 Risskov, Denmark
M. Dyrmann
Affiliation:
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 535, 5230 Odense M, Denmark
P. H. Nielsen
Affiliation:
SEGES P/S, Agro Food Park 15, 8200 Aarhus N, Denmark
R. N. Jørgensen
Affiliation:
Department of Engineering – Signal Processing, Faculty of Science and Technology, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
*
†E-mail: per.rydahl@ipmconsult.dk
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Abstract

In order to exploit potentials of 20–40% reduction of herbicide use, as documented by use of Decision Support Systems (DSS), where requirements for manual field inspection constitute a major obstacle, large numbers of digital pictures of weed infestations have been collected and analysed manually by crop advisors. Results were transferred to: 1) DSS, which determined needs for control and connected, optimized options for control returned options for control and 2) convolutional, neural networks, which in this way were trained to enable automatic analysis of future pictures, which support both field- and site-specific integrated weed management.

Keywords

integrated weed managementCrop Protection OnlineIPMwiseRoboWeedSupportautomatic determination of weeds
Type
Agri-engineering
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 860 - 864
Copyright
© The Animal Consortium 2017 

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References

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