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Automatic Sown Field Detection Using Machine Vision and Contour Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12950))

Abstract

The paper proposes a prototype of an algorithm based on the use of machine vision methods, which allows automatic identification and selection of fields sown with agricultural crops on images. The algorithm works with satellite images and consists of two stages. At the first stage, the image undergoes initial processing, after which edge detection and contour finding algorithms are applied to it. At the second stage, the obtained image areas enclosed within the contours are represented as a set of numerical and logical parameters which are used for filtering and classification of the areas.

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Acknowledgements

The proposed algorithm is implemented in Python using the OpenCV [2] image processing library and the Scikit-learn [10] machine learning library. The Matplotlib [8] library was used for graph visualization. We use Copernicus Open Access Hub [4] for satellite images.

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Shirobokov, M., Grishkin, V., Kayumova, D. (2021). Automatic Sown Field Detection Using Machine Vision and Contour Analysis. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-86960-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86959-5

  • Online ISBN: 978-3-030-86960-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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