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.
This work was financed by the Recupera 2020 Project (Spanish MINECO and EU-FEDER Funds). Research of Mr. Torres-Sánchez and Dr. Peña was financed by the FPI and Ramón y Cajal Programs, respectively. Research of Dr. Gutiérrez and Dr. Hervás-Martínez has been subsidized by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7508 project of the “Junta de Andalucía” (Spain).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
European Crop Protection Association (ECPA): Url: http://www.ecpa.eu/information-page/industry-statistics-ecpa-total, industry statistics (2015)
Regulation (EC) 1107/2009 and Directive 2009128/EC: (2009)
Horizon 2020: Url: http://ec.europa.eu/programmes/horizon2020/
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)
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)
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)
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)
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)
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)
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)
Blaschke, T.: Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1), 2–16 (2010)
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-513-16
Meier, U., ed.: Growth stages of mono-and dicotyledonous plants, 2 edn
Bunting, P., Clewley, D., Lucas, R.M., Gillingham, S.: The remote sensing and gis software library (rsgislib). Computers and Geosciences 62, 216–226 (2014)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pérez-Ortiz, M., Gutiérrez, P.A., Peña, J.M., Torres-Sánchez, J., Hervás-Martínez, C., López-Granados, F. (2015). An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and Object-Based Analysis. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-19258-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19257-4
Online ISBN: 978-3-319-19258-1
eBook Packages: Computer ScienceComputer Science (R0)