Weed segmentation from grayscale tobacco seedling images
Date Issued
2016-06-30
Author(s)
Abstract
Manual weed extraction from young seedlings is a hard manual labour process. It has to be continuously performed to increase the
yield per land unit of any agricultural product. Precise segmentation
of plant images is an important step towards creating a camera sensor for weed detection. In this paper we present a machine learning
approach for segmenting weed parts from images. A dataset has been
generated using bumblebee camera under various light conditions and
subsequently training and test patches were extracted. We have generated various texture-based descriptors and used different classification
algorithms aiming to correctly recognize weed patches. The results show
that in a case when the images are gray-scale, the light conditions are
varying, and the distance of the camera to the weeds is not constant
machine learning algorithms perform poorly
yield per land unit of any agricultural product. Precise segmentation
of plant images is an important step towards creating a camera sensor for weed detection. In this paper we present a machine learning
approach for segmenting weed parts from images. A dataset has been
generated using bumblebee camera under various light conditions and
subsequently training and test patches were extracted. We have generated various texture-based descriptors and used different classification
algorithms aiming to correctly recognize weed patches. The results show
that in a case when the images are gray-scale, the light conditions are
varying, and the distance of the camera to the weeds is not constant
machine learning algorithms perform poorly
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