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

A review of advanced machine learning methods for the detection of biotic stress in precision... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9372-7
Publisher site
See Article on Publisher Site

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.

Journal

Precision AgricultureSpringer Journals

Published: Aug 31, 2014

References

  • Weed detection in multi-spectral images of cotton fields
    Alchanatis, V; Ridel, L; Hetzroni, A; Yaroslavsky, L
  • Leaf classification in sunflower crops by computer vision and neural networks
    Arribas, JI; Sanches-Ferrero, GV; Ruiz-Ruiz, G; Gomez-Gil, J
  • Developments and directions in speech recognition and understanding, Part 1
    Baker, J; Deng, L; Glass, J; Khudanpur, S; Lee, CH; Morgan, N; O’Shaughnessy, D
  • Fusion of sensor data for the detection and differentiation of plant diseases in cucumber
    Berdugo, C; Zito, R; Paulus, S; Mahlein, A-K

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