Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby, Kjeld Jensen, Martin Peter Christiansen, Thomas Mosgaard Giselsson , Anders Krogh Mortensen, Peter Kryger Jensen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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Resumé

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.
OriginalsprogEngelsk
TidsskriftSensors
Vol/bind16
Udgave nummer11
Antal sider29
ISSN1424-8220
DOI
StatusUdgivet - 4. nov. 2016

Citer dette

Laursen, M. S., Jørgensen, R. N., Midtiby, H. S., Jensen, K., Christiansen, M. P., Mosgaard Giselsson , T., ... Jensen, P. K. (2016). Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors, 16(11). https://doi.org/10.3390/s16111848
Laursen, Morten Stigaard ; Jørgensen, Rasmus Nyholm ; Midtiby, Henrik Skov ; Jensen, Kjeld ; Christiansen, Martin Peter ; Mosgaard Giselsson , Thomas ; Mortensen, Anders Krogh ; Jensen, Peter Kryger. / Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. I: Sensors. 2016 ; Bind 16, Nr. 11.
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title = "Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops",
abstract = "The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65{\%} without measurable loss in biological effect.",
author = "Laursen, {Morten Stigaard} and J{\o}rgensen, {Rasmus Nyholm} and Midtiby, {Henrik Skov} and Kjeld Jensen and Christiansen, {Martin Peter} and {Mosgaard Giselsson}, Thomas and Mortensen, {Anders Krogh} and Jensen, {Peter Kryger}",
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Laursen, MS, Jørgensen, RN, Midtiby, HS, Jensen, K, Christiansen, MP, Mosgaard Giselsson , T, Mortensen, AK & Jensen, PK 2016, 'Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops', Sensors, bind 16, nr. 11. https://doi.org/10.3390/s16111848

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. / Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Midtiby, Henrik Skov; Jensen, Kjeld; Christiansen, Martin Peter; Mosgaard Giselsson , Thomas ; Mortensen, Anders Krogh; Jensen, Peter Kryger.

I: Sensors, Bind 16, Nr. 11, 04.11.2016.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

AU - Laursen, Morten Stigaard

AU - Jørgensen, Rasmus Nyholm

AU - Midtiby, Henrik Skov

AU - Jensen, Kjeld

AU - Christiansen, Martin Peter

AU - Mosgaard Giselsson , Thomas

AU - Mortensen, Anders Krogh

AU - Jensen, Peter Kryger

PY - 2016/11/4

Y1 - 2016/11/4

N2 - The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

AB - The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

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