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28 pages, 49792 KiB  
Article
Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops
by Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby, Kjeld Jensen, Martin Peter Christiansen, Thomas Mosgaard Giselsson, Anders Krogh Mortensen and Peter Kryger Jensen
Sensors 2016, 16(11), 1848; https://doi.org/10.3390/s16111848 - 4 Nov 2016
Cited by 23 | Viewed by 8026
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
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