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Sensors 2016, 16(11), 1848; doi:10.3390/s16111848

Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops

1
Department of Engineering, Aarhus University, 8000 Aarhus, Denmark
2
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark
3
AGROINTELLI, 8200 Aarhus, Denmark
4
Danish Technological Institute, Robot Technology, 5230 Odense, Denmark
5
Department of Agroecology—Crop Health, Aarhus University, 4200 Slagelse, Denmark
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Received: 11 August 2016 / Revised: 17 October 2016 / Accepted: 26 October 2016 / Published: 4 November 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)

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. View Full-Text
Keywords: weed crop discrimination; grid sprayer; herbicide reduction; site specific; sprayer boom; monocot and dicot coverage ratio vision (MoDiCoVi) weed crop discrimination; grid sprayer; herbicide reduction; site specific; sprayer boom; monocot and dicot coverage ratio vision (MoDiCoVi)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Laursen, M.S.; Jørgensen, R.N.; Midtiby, H.S.; Jensen, K.; Christiansen, M.P.; Giselsson, T.M.; Mortensen, A.K.; Jensen, P.K. Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors 2016, 16, 1848.

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