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

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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.

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Notes

  1. In this article, the following notation is used: matrices are noted as bold upper-case letters in italics, vectors as bold lower-case letters in italics and scalars as lower-case letters in italics. Models and distributions which can have various representations are noted as bold upper-case letters.

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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Acknowledgments

The authors would like to thank Prof. Heiner Goldbach for valuable comments and suggestions to improve the quality of this paper. This work was conducted within the Research Training Group 722 ‘Information Techniques for Precision Crop Protection’, funded by the German Research Foundation (DFG), and the Network of excellence in agriculture and nutrition research, CROP.SENSE.net funded by the German Federal Ministry of Education and Research (BMBF) (Funding code: 0315529). The authors further acknowledge the funding of the CROP.SENSe.net project in the context of Ziel 2-Programms NRW 2007–2013 “Regionale Wettbewerbsfähigkeit und Beschäftigung (EFRE)” under the aegis of the Ministry for Innovation, Science and Research (MIWF) of the North Rhine Westphalia (NRW) federal state as well as the receipt of European Union Funds for regional development (EFRE) (005-1103-0018) for the preparation of this manuscript.

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  1. Institute of Geodesy and Geoinformation (IGG) - Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115, Bonn, Germany

    Jan Behmann, Till Rumpf, Christoph Römer & Lutz Plümer

  2. Institute of Crop Science and Resource Conservation (INRES) - Phytomedicine, University of Bonn, Nussallee 9, 53115, Bonn, Germany

    Anne-Katrin Mahlein

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  1. Jan Behmann
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Correspondence to Jan Behmann.

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Jan Behmann and Anne-Katrin Mahlein have contributed equally to this work.

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Behmann, J., Mahlein, AK., Rumpf, T. et al. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agric 16, 239–260 (2015). https://doi.org/10.1007/s11119-014-9372-7

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