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Weed Segmentation from Grayscale Tobacco Seedling Images

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Advances in Robot Design and Intelligent Control (RAAD 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 540))

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Abstract

Manual weed extraction from young seedlings is a hard manual labour process. It has to be continuously performed to increase the yield per land unit of any agricultural product. Precise segmentation of plant images is an important step towards creating a camera sensor for weed detection. In this paper we present a machine learning approach for segmenting weed parts from images. A dataset has been generated using bumblebee camera under various light conditions and subsequently training and test patches were extracted. We have generated various texture-based descriptors and used different classification algorithms aiming at correctly recognizing weed patches. The results show that in a case when the images are grayscale, the light conditions are varying, and the distance of the camera to the weeds is not constant machine learning algorithms perform poorly.

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Acknowledgment

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

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Saints Cyril and Methodius in Skopje, Skopje, Macedonia

    Petre Lameski, Eftim Zdravevski & Andrea Kulakov

Authors
  1. Petre Lameski
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  2. Eftim Zdravevski
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  3. Andrea Kulakov
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Corresponding author

Correspondence to Petre Lameski .

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Editors and Affiliations

  1. Robotics Laboratory, Mihajlo Pupin Institute, Belgrade, Serbia

    Aleksandar Rodić

  2. Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

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Lameski, P., Zdravevski, E., Kulakov, A. (2017). Weed Segmentation from Grayscale Tobacco Seedling Images. In: Rodić, A., Borangiu, T. (eds) Advances in Robot Design and Intelligent Control. RAAD 2016. Advances in Intelligent Systems and Computing, vol 540. Springer, Cham. https://doi.org/10.1007/978-3-319-49058-8_28

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  • DOI: https://doi.org/10.1007/978-3-319-49058-8_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49057-1

  • Online ISBN: 978-3-319-49058-8

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