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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

Been, T, Berti, A, Evans, N, Gouache, D, Volkmar, G, Jensen, JE, Kapsa, J, Levay, N, Munier-Jolain, N, Nibouche, S, Raynal, M and Rydahl, P 2009. Review of new technologies critical to effective implementation of Decision Support Systems (DSS’s) and Farm Management Systems (FMS’s) Aarhus University, Denmark, 6th March 2009. http://www.endure-network.eu/content/download/4803/39494/file/Review%20of%20new%20technologies%20critical%20to%20effective%20implementation%20of%20decision%20support%20systems%20and%20farm%20management%20systems.pdf Google Scholar
Dyrmann, M, Jørgensen, RM and Midtiby, HS 2017. RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. In this volume.CrossRefGoogle Scholar
Dyrmann, M and Jørgensen, RN 2015. “RoboWeedSupport: Weed Recognition for Reduction of Herbicide Consumption.” In Precision Agriculture ’15, edited by JV Stafford, 571–578. Wageningen Academic Publishers Books, The Netherlands.CrossRefGoogle Scholar
Dyrmann, Mads, Karstoft, Henrik and Midtiby, Henrik Skov 2016a. “Plant Species Classification Using Deep Convolutional Neural Network.” Biosystems Engineering 151, 72–80.Google Scholar
Dyrmann, Mads, Mortensen, Anders Krogh, Midtiby, Henrik Skov and Jørgensen, Rasmus Nyholm 2016b. “Pixel-Wise Classification of Weeds and Crop in Images by Using a Fully Convolutional Neural Network.” In International Conference on Agricultural Engineering 2016. Aarhus University. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_semanticsegmentation.pdf Google Scholar
Jørgensen, LN, Noe, E, Langvad, AM, Jensen, JE, Orum, JE and Rydahl, P 2007. Decision support systems: barriers and farmers’ need for support. EPPO Bulletin 37 (2), 374–377.Google Scholar
Laursen, MS, Jorgensen, RN, Dyrmann, M and Poulsen, RN 2017. RoboWeedSupport - Sub millimeter weed image acquisition in cereal crops with speeds up till 50 km/h. In this volume.Google Scholar
Montull, JM 2016. Adapting the Decision Support System CPOWeeds to optimize weed control in northern Spanish conditions. PhD dissertation, Departament de Hortofruticulture, Botanica in Jardineria, Universitat de Lleida, Spain.Google Scholar
Rydahl, P and Bøjer, 2016. ‘IPMwise’, customized for conditions in Denmark (dk.ipmwise.com).Google Scholar
Rydahl, P 2004. A Danish decision support system for integrated management of weeds. Aspects of Applied Biology 72, Advances in Applied Biology: Providing New Opportunities for Consumers and Producers in the 21st Century, 2004, p. 43–53.Google Scholar
Sønderskov, M, Fritzsche, R, de Mol, F, Gerowitt, B, Goltermann, S, Kierzek, R, Krawczyk, R, Bøjer, OM and Rydahl, P 2015. DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions – Field experimental results, Crop Protection, Volume 76, October 2015, Pp 15–23.CrossRefGoogle Scholar
Sønderskov, M, Kudsk, P, Mathiassen, SK, Bøjer, OM and Rydahl, P 2014. Decision Support System for Optimized Herbicide Dose in Spring Barley. Weed Technology: January–March 28 (1), 19–27.Google Scholar
Tørresen, KS, Netland, J and Rydahl, P 2004. Norsk utgave av det danske beslutningsstøtte-systemet Planteværn Online for ugrassprøyting i korn. Grønn kunnskap 8 (2), 100–109.Google Scholar