|Reference : Improving in-row weed detection in multispectral stereoscopic images|
|Scientific journals : Article|
|Life sciences : Environmental sciences & ecology|
|Improving in-row weed detection in multispectral stereoscopic images|
|Piron, Alexis [Université de Liège - ULg > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech >]|
|Leemans, Vincent [Université de Liège - ULg > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech >]|
|Lebeau, Frédéric [Université de Liège - ULg > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech >]|
|Destain, Marie-France [Université de Liège - ULg > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech >]|
|Computers & Electronics in Agriculture|
|Yes (verified by ORBi)|
|[en] stereovision ; multispectral ; weed detection|
|[en] Previous research has shown that plant height and spectral reflectance are relevant features to classify
crop and weeds in organic carrots: classification based on height gave a classification accuracy (CA) of up
to 83% while classification based on a combination of three multispectral bands gave a CA of 72%.
The first goal of this study was to examine the simultaneous use of both height and multispectral
parameters. It was found that classification rate was only slightly improved when using a feature set
comprising both height and multispectral data (2%).
The second goal of this study was to improve the detection method based on plant height by setting
an automatic threshold between crop and weeds heights, in their early growth stage. This threshold
was based on crop row determination and peak detection in plant height probability density function,
corresponding to the homogeneous crop population. Using this method, the CA was 82% while the CA
obtained with optimal plant height limits is only slightly higher at 86%.
|Gembloux Agro-Bio Tech|
|Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE|
|File(s) associated to this reference|
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