A review of advanced machine learning methods
for the detection of biotic stress in precision crop
Published online: 31 August 2014
Ó Springer Science+Business Media New York 2014
Abstract Effective crop protection requires early and accurate detection of biotic stress.
In recent years, remarkable results have been achieved in the early detection of weeds,
plant diseases and insect pests in crops. These achievements are related both to the
development of non-invasive, high resolution optical sensors and data analysis methods
that are able to cope with the resolution, size and complexity of the signals from these
sensors. Several methods of machine learning have been utilized for precision agriculture
such as support vector machines and neural networks for classiﬁcation (supervised
learning); k-means and self-organizing maps for clustering (unsupervised learning). These
methods are able to calculate both linear and non-linear models, require few statistical
assumptions and adapt ﬂexibly to a wide range of data characteristics. Successful appli-
cations include the early detection of plant diseases based on spectral features and weed
detection based on shape descriptors with supervised or unsupervised learning methods.
This review gives a short introduction into machine learning, analyses its potential for
precision crop protection and provides an overview of instructive examples from different
ﬁelds of precision agriculture.
Keywords Machine learning Á Stress detection Á Optical sensors Á Data analysis Á Plant
diseases Á Weed detection
Jan Behmann and Anne-Katrin Mahlein have contributed equally to this work.
J. Behmann (&) Á T. Rumpf Á C. Ro
mer Á L. Plu
Institute of Geodesy and Geoinformation (IGG) - Geoinformation, University of Bonn, Meckenheimer
Allee 172, 53115 Bonn, Germany
Institute of Crop Science and Resource Conservation (INRES) - Phytomedicine, University of Bonn,
Nussallee 9, 53115 Bonn, Germany
Precision Agric (2015) 16:239–260