In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the appearances and the differences between the plants used by humans, into a formal, computer understandable form. Also it is not possible to reliably detect structures, like leaves and branches in 3D data provided by our sensor. One approach to solve this problem is to learn how to classify the species by using a set of example plants and machine learning methods. In this paper we are introducing a method for distinguishing plant species using a 3D LIDAR sensor and supervised learning. For that we have developed a set of size and rotation invariant features and evaluated experimentally which are the most descriptive ones. Besides these features we have also compared different learning methods using the toolbox Weka. It turned out that the best methods for our application are simple logistic regression functions, support vector machines and neural networks. In our experiments we used six different plant species, typically available at common nurseries, and about 20 examples of each species. In the laboratory we were able to identify over 98% of these plants correctly.
@inproceedings{weiss2010icmla,
author = {Ulrich Weiss and Peter Biber and Stefan Laible and Karsten Bohlmann
and Andreas Zell},
title = {Plant Species Classification Using a 3D LIDAR Sensor and Machine
Learning},
booktitle = {Machine Learning and Applications (ICMLA), 2010 Ninth International
Conference on},
year = {2010},
pages = {339--345},
address = {Washington, D.C., USA},
month = {dec.},
organization = {IEEE Computer Society},
publisher = {IEEE},
abstract = {In the domain of agricultural robotics, one major application is crop
scouting, e.g., for the task of weed control. For this task a key
enabler is a robust detection and classification of the plant and
species. Automatically distinguishing between plant species is a
challenging task, because some species look very similar. It is also
difficult to translate the symbolic high level description of the
appearances and the differences between the plants used by humans,
into a formal, computer understandable form. Also it is not possible
to reliably detect structures, like leaves and branches in 3D data
provided by our sensor. One approach to solve this problem is to
learn how to classify the species by using a set of example plants
and machine learning methods. In this paper we are introducing a
method for distinguishing plant species using a 3D LIDAR sensor and
supervised learning. For that we have developed a set of size and
rotation invariant features and evaluated experimentally which are
the most descriptive ones. Besides these features we have also compared
different learning methods using the toolbox Weka. It turned out
that the best methods for our application are simple logistic regression
functions, support vector machines and neural networks. In our experiments
we used six different plant species, typically available at common
nurseries, and about 20 examples of each species. In the laboratory
we were able to identify over 98% of these plants correctly.},
doi = {10.1109/ICMLA.2010.57},
keywords = {3D LIDAR sensor;Weka;agricultural robotics;crop scouting;logistic
regression;machine learning;neural networks;plant species classification;supervised
learning;support vector machines;agriculture;biology computing;botany;learning
(artificial intelligence);neural nets;optical radar;robots;support
vector machines;},
url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/weiss2010icmla.pdf}
}