Abstract
Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andrew, M.E., Ustin, S.L.: The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens. Environ. 112, 4301–4317 (2008)
Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogrammetry Remote Sens. 65, 2–16 (2010)
Blaschke, T., Burnett, C., Pekkarinen, A.: New contextual approaches using image segmentation for object-based classification. In: De Meer, F., de Jong, S. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain, pp. 211–236. Kluver Academic Publishers, Dordrecht (2004)
Duro, D.C., Franklin, S.E., Dubé, M.G.: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 118, 259–272 (2012)
Egorov, A.V., Hansen, M.C., Roya, D.P., Kommareddy, A., Potapov, P.V.: Image interpretation-guided supervised classification using nested segmentation. Remote Sens. Environ. 165, 135–147 (2015)
Franco, C., Pedersen, S.M., Papaharalampos, H., Ørum, J.E.: The value of precision for image-based decision support in weed management. Precis. Agric. 18, 366–382 (2017)
Gómez, D., Biging, G., Montero, J.: Accuracy statistics for judging soft classification. Int. J. Remote Sens. 29, 693–709 (2008)
Gómez, D., Biging, G.S., Montero, J.: Accuracy assessment for soft classification maps. In: Wang, G., Weng, Q. (eds.) Remote Sensing of Natural Resources, pp. 57–86. CRC Press, Boca Raton (2014)
Guada, C., Gómez, D., Rodríguez, J.T., Yáñez, J., Montero, J.: Classifying image analysis techniques from their output. Int. J. Comput. Intell. Syst. 9, 43–68 (2016)
Guada, C., Zarrazola, E., Yáñez, J., Rodríguez, J.T., Gómez, D., Montero, J.: A novel edge detection algorithm based on a hierarchical graph-partition approach. J. Intell. Fuzzy Syst. 34(3), 1875–1892 (2018, in press)
Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall International, Upper Saddle River (1999)
Lamb, D.W., Brown, R.B.: Remote-sensing and mapping of weeds in crops. Agri. Eng. Res. 78, 117–125 (2001)
Moran, M.S., Inoue, Y., Barnes, E.M.: Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61, 319–346 (1997)
Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q.: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115, 1145–1161 (2011)
Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., Streibig, J.C.: Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res. 53, 242–248 (2013)
Robertson, L.D., King, D.J.: Comparison of pixel and object based classification in land cover mapping. Int. J. Remote Sens. 32, 1505–1529 (2011)
Seelan, S.K., Laguette, S., Casady, G.M., Seielstad, G.A.: Remote sensing applications for precision agriculture: a learning community approach. Remote Sens. Environ. 88, 157–169 (2003)
Slaughter, D.C., Giles, D.K., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61, 63–78 (2008)
Sørensen, R., Rasmussen, J., Nielsen, J., Jørgensen, R.N.: Thistle detection using convolutional neural networks. In: Proceedings EFITA-WCCA 2017 Conference, Montpellier, France, paper 75, 2–6 July 2017
Tellaeche, A., Pajares, G., Burgos-Artizzu, X.P., Ribeiro, A.: A computer vision approach for weeds identification through support vector machines. Appl. Soft Comput. 11, 908–915 (2011)
Whiteside, T.G., Boggs, G.S., Maier, S.W.: Comparing object-based and pixel-based classifications for mapping savannas. Appl. Earth Obs. Geoinf. 13, 884–893 (2011)
Acknowledgement
This research has been partially supported by the Government of Spain (grant TIN2015-66471-P), the Government of Madrid (grant S2013/ICE-2845, CASICAM-CM), Complutense University (UCM research group 910149), and the Danish Environmental Protection Agency.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Franco, C. et al. (2018). Automatic Detection of Thistle-Weeds in Cereal Crops from Aerial RGB Images. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-91479-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91478-7
Online ISBN: 978-3-319-91479-4
eBook Packages: Computer ScienceComputer Science (R0)