Sparse-Based Feature Selection for Discriminating Between Crops and Weeds Using Field Images

  • Daniel Guillermo García-MurilloEmail author
  • Andrés M. Álvarez
  • David Cárdenas-Peña
  • William Hincapie-Restrepo
  • German Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


Control of weed growing in yields is a critical task for reducing crop losses. Recently, image-based systems attempt to discriminate between crops and weeds from a set of features. Although some features have a physiological meaning, most of them are redundant or noisy. Therefore, selecting relevant features must result in interpretable and accurate results while reducing the computational complexity of the system. In this work, we introduce a sparse-based feature selection approach using the Lasso operator that eliminates noisy features aiming to improve the classification of crops. We evaluate our proposal on the Crop/Weed Field Image Dataset, for which we tune the parameters by maximizing the accuracy and minimizing feature dimension. Achieved performance results evidence that our proposed approach improves discrimination in comparison with other feature selection approaches, with the benefit of providing interpretability in weed/crop discrimination tasks.


Lasso Feature Selection Weed/crop discrimination HOG 



“This work was developed under the research project CARACTERIZACIÓN DE CULTIVOS AGRÍCOLAS MEDIANTE ESTRATEGIAS DE TELEDETECCIÓN Y TÉCNICAS DE PROCESAMIENTO DE IMÁGENES (Hermes-36719) funded by Universidad Nacional de Colombia”.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel Guillermo García-Murillo
    • 1
    Email author
  • Andrés M. Álvarez
    • 1
  • David Cárdenas-Peña
    • 2
  • William Hincapie-Restrepo
    • 3
  • German Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Automatic Research GroupUniversidad Tecnológica de PereiraPereiraColombia
  3. 3.Physicochemistry of Terrestrial Fluids GroupUniversidad de CaldasManizalesColombia

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