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Automatic arable land detection with supervised machine learning

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Abstract

In Precision Agriculture one of the basic tasks is the classification of land zones in either arable or non-arable land. Several studies have been conducted using data obtained from soil analysis or local exploration of the parcels. However, sometimes only data from satellite images are available and then the problem not only becomes more challenging but also more interesting to solve because it is much more cost-effective. In this paper, we consider different spectral and thermal bands from the Landsat 8 satellite images corresponding to a vineyard located in Galicia, a region in Northeastern Spain, and apply a range of supervised Machine Learning methods to classify the different land zones. We conclude that an adequate choice of the algorithm parameters together with feature selection techniques can yield a classification that is both highly effective and efficient.

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Authors and Affiliations

  1. Department of Computer Science, University of Oviedo, Oviedo, Spain

    I. Díaz & E. F. Combarro

  2. Terras Gauda, O Rosal, Spain

    E. R. Canas

  3. SERESCO, Oviedo, Spain

    R. B. Arango & A. Campos

Authors
  1. R. B. Arango
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  2. I. Díaz
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  3. A. Campos
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  4. E. R. Canas
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  5. E. F. Combarro
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Corresponding author

Correspondence to I. Díaz.

Additional information

Communicated by: H. A. Babaie

This work has been supported Farm-Oriented Open Data in Europe (FOODIE) Pilot B from European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no. 621074.

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Arango, R.B., Díaz, I., Campos, A. et al. Automatic arable land detection with supervised machine learning. Earth Sci Inform 9, 535–545 (2016). https://doi.org/10.1007/s12145-016-0270-6

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