Philipp Lottes, Markus Hoeferlin, Slawomir Sander, Martin Müter, Peter Schulze Lammers, and Cyrill Stachniss
An Effective Classification System for Separating
Sugar Beets and Weeds for Precision Farming Applications
Abstract:
Robots for precision farming have the potential
to reduce the reliance on herbicides and pesticides through
selectively spraying individual plants or through manual weed
removal. To achieve this, the value crops and the weeds must
be identified by the robot’s perception system to trigger the
actuators for spraying or removal. In this paper, we address
the problem of detecting the sugar beet plants as well as weeds
using a camera installed on a mobile robot operating on a field.
We propose a system that performs vegetation detection, feature
extraction, random forest classification, and smoothing through
a Markov random field to obtain an accurate estimate of the
crops and weeds. We implemented and thoroughly evaluated
our system on a real farm robot on different sugar beet
fields and illustrate that our approach allows for accurately
identifying the weed on the field.
Bibtex:
@inproceedings{lottes2016effective,
title={An Effective Classification System for Separating Sugar Beets and Weeds for Precision Farming Applications},
author={Lottes, P and Hoeferlin, M and Sander, S and M{\"u}ter, M and Lammers, P Schulze and Stachniss, C},
booktitle={Proceedings of the IEEE Int. Conf. on Robotics \& Automation (ICRA)},
year={2016}
}
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