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SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification

Author

Listed:
  • Jordi Creus Tomàs

    (University of Campinas, Institute of Computing, Campinas, Brazil)

  • Fabio Augusto Faria

    (Federal University of São Paulo, São José dos Campos, Brazil)

  • Júlio César Dalla Mora Esquerdo

    (Brazilian Agricultural Research Corporation, Embrapa Agricultural Informatics, Campinas, Brazil)

  • Alexandre Camargo Coutinho

    (Brazilian Agricultural Research Corporation, Embrapa Agricultural Informatics, Campinas, Brazil)

  • Claudia Bauzer Medeiros

    (University of Campinas, Institute of Computing, Campinas, Brazil)

Abstract

This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.

Suggested Citation

  • Jordi Creus Tomàs & Fabio Augusto Faria & Júlio César Dalla Mora Esquerdo & Alexandre Camargo Coutinho & Claudia Bauzer Medeiros, 2017. "SiRCub, A Novel Approach to Recognize Agricultural Crops Using Supervised Classification," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 8(4), pages 20-36, October.
    • Handle: RePEc:igg:jaeis0:v:8:y:2017:i:4:p:20-36
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