Precision Agriculture

, Volume 16, Issue 3, pp 239–260 | Cite as

A review of advanced machine learning methods for the detection of biotic stress in precision crop protection

  • Jan Behmann
  • Anne-Katrin Mahlein
  • Till Rumpf
  • Christoph Römer
  • Lutz Plümer


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 applications 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.


Machine learning Stress detection Optical sensors Data analysis Plant diseases Weed detection 



Food and agriculture organization of the United Nations


European and Mediterranean plant protection organization


Support vector machine


Support vector regression


Radial basis function kernel


Neural networks


Self-organizing maps


Vegetation index


Normalized difference vegetation index


Principal component analysis


Principal components


Linear discriminant analysis


Quadratic discriminant analysis


Partial least squares


Near infrared


Red, green and blue color image


LAB-color space: lightness (L), a and b for color-component dimensions


YCBCR-color space: luminance (Y), blue-yellow chrominance (CB), red-green chrominance (CR)


HSV-color space: hue, saturation, value


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jan Behmann
    • 1
  • Anne-Katrin Mahlein
    • 2
  • Till Rumpf
    • 1
  • Christoph Römer
    • 1
  • Lutz Plümer
    • 1
  1. 1.Institute of Geodesy and Geoinformation (IGG) - GeoinformationUniversity of BonnBonnGermany
  2. 2.Institute of Crop Science and Resource Conservation (INRES) - PhytomedicineUniversity of BonnBonnGermany

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