Philipp Lottes, Markus Hoeferlin, Slawomir Sander, and Cyrill Stachniss
Effective Vision-Based Classification for Separating
Sugar Beets and Weeds for Precision Farming
Abstract:
Using robots in precision farming has the potential to reduce the reliance on herbicides and
pesticides through selectively spraying individual plants or through manual weed removal.
A prerequisite for that is the ability of the robot to separate and identify the value crops
and the weeds on the field. Based on the output of the robot's perception system, it can
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 field
robot. We propose a system that performs vegetation detection, local as well as object-based
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{lottes2016sugarbeetsjfr,
title={Effective Vision-Based Classification for Separating
Sugar Beets and Weeds for Precision Farming},
author={Lottes, P and Hoeferlin, M and Sander, S and Stachniss, C},
booktitle={Journal of Field Robotics},
year={2016}
}
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