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EPIA Conference on Artificial Intelligence

EPIA 2019: Progress in Artificial Intelligence pp 109–120Cite as

Vineyard Segmentation from Satellite Imagery Using Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Abstract

Steep slope vineyards are a complex scenario for the development of ground robots due to the harsh terrain conditions and unstable localization systems. Automate vineyard tasks (like monitoring, pruning, spraying, and harvesting) requires advanced robotic path planning approaches. These approaches usually resort to Simultaneous Localization and Mapping (SLAM) techniques to acquire environment information, which requires previous navigation of the robot through the entire vineyard. The analysis of satellite or aerial images could represent an alternative to SLAM techniques, to build the first version of occupation grid map (needed by robots). The state of the art for aerial vineyard images analysis is limited to flat vineyards with straight vine’s row. This work considers a machine learning based approach (SVM classifier with Local Binary Pattern (LBP) based descriptor) to perform the vineyard segmentation from public satellite imagery. In the experiments with a dataset of satellite images from vineyards of Douro region, the proposed method achieved accuracy over 90%.

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Notes

  1. 1.

    ROS - http://www.ros.org/.

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Acknowledgements

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013 and by through ANI - Agência Nacional de Inovação (Portuguese National Agency of Innovation) as part of project “ROMOVI: POCI-01-0247-FEDER-017945”.

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Authors and Affiliations

  1. INESC TEC - INESC Technology and Science, Porto, Portugal

    Luís Santos, Filipe N. Santos, Vitor Filipe & Pranjali Shinde

  2. UTAD - University of Trás-os-Montes e Alto Douro, Vila Real, Portugal

    Luís Santos & Vitor Filipe

Authors
  1. Luís Santos
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  2. Filipe N. Santos
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  3. Vitor Filipe
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  4. Pranjali Shinde
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Corresponding author

Correspondence to Luís Santos .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Santos, L., Santos, F.N., Filipe, V., Shinde, P. (2019). Vineyard Segmentation from Satellite Imagery Using Machine Learning. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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