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Vision System for Robotized Weed Recognition in Crops and Grasslands

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Computer Vision Systems (ICVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10528))

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Abstract

In this paper, we introduce a novel vision system for robotized weed control on various weed recognition tasks. Initially, we present a robotic platform and its camera setup, that can be used in crop-based and grassland-based weed control tasks. Then, we develop our proposed vision system for robotic application, using a weed recognition framework. The resulting system derives from a sequence of state-of-the-art processes including image preprocessing, feature extraction and detection, codebook learning, feature encoding, image representation and classification. Our novel system is optimized using a dataset which represents a crop-based weed control problem of thistles in sugar beet plantation. Moreover, we apply the proposed vision system to a grassland-based weed recognition problem, the control of the Broad-leaved Dock (Rumex obtusifolius L.). It is experimentally shown that our proposed visual system yields state-of-the-art recognition in both examined datasets, while presenting advantages in terms of autonomy and precision over competing methodologies.

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Acknowledgements

This work has been supported by the DockWeeder project (project ID: 30079), administered through the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 618123 [ICT-AGRI 2]. The project has received funding from the Ministry of Economic Affairs (The Netherlands), from the Federal Office for Agriculture (Switzerland), and from Innovation Fund Denmark, the Ministry of Science, Innovation and Higher Education (Denmark).

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Authors and Affiliations

  1. Department of Materials and Production, Aalborg University, Copenhagen, Denmark

    Tsampikos Kounalakis & Lazaros Nalpantidis

  2. Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark

    Georgios A. Triantafyllidis

Authors
  1. Tsampikos Kounalakis
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  2. Georgios A. Triantafyllidis
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  3. Lazaros Nalpantidis
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Correspondence to Tsampikos Kounalakis .

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Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Ming Liu

  2. Harbin Institute of Technology, Shenzhen, China

    Haoyao Chen

  3. Technische Universtiät Wien, Vienna, Austria

    Markus Vincze

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Kounalakis, T., Triantafyllidis, G.A., Nalpantidis, L. (2017). Vision System for Robotized Weed Recognition in Crops and Grasslands. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_43

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  • DOI: https://doi.org/10.1007/978-3-319-68345-4_43

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