Advertisement

An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and Object-Based Analysis

  • María Pérez-OrtizEmail author
  • Pedro Antonio Gutiérrez
  • Jose Manuel Peña
  • Jorge Torres-Sánchez
  • César Hervás-Martínez
  • Francisca López-Granados
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

Weed control in precision agriculture refers to the design of site-specific control treatments according to weed coverage and it is very useful to minimise costs and environmental risks. The crucial component is to provide precise and timely weed maps via weed monitoring. This paper compares different approaches for weed mapping using imagery from Unmanned Aerial Vehicles in sunflower crops. We explore different alternatives, such as object-based analysis, which is a strategy that is spreading rapidly in the field of remote sensing. The usefulness of these approaches is tested by considering support vector machines, one of the most popular machine learning classifiers. The results show that the object-based analysis is more promising than the pixel-based one and demonstrate that both the features related to vegetation indexes and those related to the shape of the objects are meaningful for the problem.

Keywords

Unmanned Aerial Vehicles Object-based analysis Weed mapping Image segmentation Support vector machines 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    European Crop Protection Association (ECPA): Url: http://www.ecpa.eu/information-page/industry-statistics-ecpa-total, industry statistics (2015)
  2. 2.
    Regulation (EC) 1107/2009 and Directive 2009128/EC: (2009)Google Scholar
  3. 3.
  4. 4.
    de Castro, A.I., Jurado-Expósito, M., Peña-Barragán, J.M., López-Granados, F.: Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precision Agriculture 13(3), 302–321 (2012)CrossRefGoogle Scholar
  5. 5.
    Castillejo-González, I.L., Peña, J.M., Jurado-Expósito, M., Mesas-Carrascosa, F.J., López-Granados, F.: Evaluation of pixel-and object-based approaches for mapping wild oat (avena sterilis) weed patches in wheat fields using quickbird imagery for site-specific management. European Journal of Agronomy 59, 57–66 (2014)CrossRefGoogle Scholar
  6. 6.
    Moranduzzo, T., Melgani, F.: Automatic car counting method for unmanned aerial vehicle images. IEEE Transactions on Geoscience and Remote Sensing 52(3), 1635–1647 (2014)CrossRefGoogle Scholar
  7. 7.
    Lucieer, A., Turner, D., King, D.H., Robinson, S.A.: Using an unmanned aerial vehicle (uav) to capture micro-topography of antarctic moss beds. International Journal of Applied Earth Observation and Geoinformation 27, 53–62 (2014)CrossRefGoogle Scholar
  8. 8.
    Peña, J.M., Torres-Sánchez, J., de Castro, A.I., Kelly, M., López-Granados, F.: Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (uav) images. PLoS One 8(10), e77151 (2013)CrossRefGoogle Scholar
  9. 9.
    Torres-Sánchez, J., López-Granados, F., De Castro, A.I., Peña, J.M.: Configuration and specifications of an unmanned aerial vehicle (uav) for early site specific weed management. PLoS One 8(3), e58210 (2013)CrossRefGoogle Scholar
  10. 10.
    Torres-Sánchez, J., Peña, J., de Castro, A., López-Granados, F.: Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from uav. Computers and Electronics in Agriculture 103, 104–113 (2014)CrossRefGoogle Scholar
  11. 11.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1), 2–16 (2010)CrossRefGoogle Scholar
  12. 12.
    Barla, A., Odone, F., Verri, A.: Histogram intersection kernel for image classification. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429). vol. 3, IEEE (2003). III-513-16Google Scholar
  13. 13.
    Meier, U., ed.: Growth stages of mono-and dicotyledonous plants, 2 ednGoogle Scholar
  14. 14.
    Bunting, P., Clewley, D., Lucas, R.M., Gillingham, S.: The remote sensing and gis software library (rsgislib). Computers and Geosciences 62, 216–226 (2014)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  16. 16.
    Johnson, B., Xie, Z.: Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing 66, 473–483 (2011)CrossRefGoogle Scholar
  17. 17.
    Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38(1), 259–269 (1995)CrossRefGoogle Scholar
  18. 18.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)CrossRefGoogle Scholar
  19. 19.
    Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66(3), 247–259 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • María Pérez-Ortiz
    • 1
    Email author
  • Pedro Antonio Gutiérrez
    • 2
  • Jose Manuel Peña
    • 1
  • Jorge Torres-Sánchez
    • 1
  • César Hervás-Martínez
    • 2
  • Francisca López-Granados
    • 1
  1. 1.Institute for Sustainable AgricultureCSICCórdobaSpain
  2. 2.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain

Personalised recommendations