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
Article

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 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.

Keywords

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

Abbreviations

FAO

Food and agriculture organization of the United Nations

EPPO

European and Mediterranean plant protection organization

SVM

Support vector machine

SVR

Support vector regression

Rbf

Radial basis function kernel

NN

Neural networks

SOM

Self-organizing maps

VI

Vegetation index

NDVI

Normalized difference vegetation index

PCA

Principal component analysis

PCs

Principal components

LDA

Linear discriminant analysis

QDA

Quadratic discriminant analysis

PLS

Partial least squares

NIR

Near infrared

RGB

Red, green and blue color image

LAB

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

YCBCR

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

HSV

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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