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Data Mining and Pattern Recognition in Agriculture

Abstract

Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture.

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Notes

  1. Note that, for the time being, our methodology is tailored towards basic agricultural research carried out in the lab; yet, as costs of hyper-spectral sensors are decreasing, corresponding technologies may soon be applicable in outdoor scenarios.

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Acknowledgements

Parts of the work reported here were conducted within the project SmartDDS which is funded by the Bundesanstalt für Landwirschaft und Ernährung. Kristian Kersting was supported by the Fraunhofer ATTRACT fellowship “Statistical Relational Activity Mining”. The authors gratefully acknowledge this support.

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Correspondence to Christian Bauckhage.

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Bauckhage, C., Kersting, K. Data Mining and Pattern Recognition in Agriculture. Künstl Intell 27, 313–324 (2013). https://doi.org/10.1007/s13218-013-0273-0

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Keywords

  • Drought Stress
  • Local Binary Pattern
  • Leaf Spot
  • Precision Farming
  • Cercospora Leaf Spot