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International Conference on ICT Innovations

ICT Innovations 2017: ICT Innovations 2017 pp 112–119Cite as

Weed Detection Dataset with RGB Images Taken Under Variable Light Conditions

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 778))

Abstract

Weed detection from images has received a great interest from scientific communities in recent years. However, there are only a few available datasets that can be used for weed detection from unmanned and other ground vehicles and systems. In this paper we present a new dataset (i.e. Carrot-Weed) for weed detection taken under variable light conditions. The dataset contains RGB images from young carrot seedlings taken during the period of February in the area around Negotino, Republic of Macedonia. We performed initial analysis of the dataset and report the initial results, obtained using convolutional neural network architectures.

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Acknowledgments

The work presented in this paper was partially financed by the University of Sts. Cyril and Methodius in Skopje, Macedonia, Faculty of Computer Science and Engineering.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Sts. Cyril and Methodius in Skopje, Ruger Boskovik 16, 1000, Skopje, Macedonia

    Petre Lameski, Eftim Zdravevski, Vladimir Trajkovik & Andrea Kulakov

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  1. Petre Lameski
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  2. Eftim Zdravevski
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  3. Vladimir Trajkovik
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  4. Andrea Kulakov
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Corresponding author

Correspondence to Petre Lameski .

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Editors and Affiliations

  1. Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia

    Dimitar Trajanov

  2. Computer Science and Engineering, Ss. Cyril and Methodius University , Skopje, Macedonia

    Verica Bakeva

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Lameski, P., Zdravevski, E., Trajkovik, V., Kulakov, A. (2017). Weed Detection Dataset with RGB Images Taken Under Variable Light Conditions. In: Trajanov, D., Bakeva, V. (eds) ICT Innovations 2017. ICT Innovations 2017. Communications in Computer and Information Science, vol 778. Springer, Cham. https://doi.org/10.1007/978-3-319-67597-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-67597-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67596-1

  • Online ISBN: 978-3-319-67597-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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