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Semantic Segmentation of Small Region of Interest for Agricultural Research Applications

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

The artificial intelligence and, in particular, the artificial neural networks proved to be useful tools in the field of computer vision, with promising results of applications in various domains, such as: industry, agriculture, medicine, transport, and environment. Detecting and locating crops using images received from aerial robots can make a positive contribution to assessing possible damage, reducing losses and minimizing analysis time. The paper proposed different implementation of the conditional generative adversarial network to better accomplish the task of semantic segmentation the agricultural region of interest. To this end the images were acquired by unmanned aerial vehicles. The network consists of a generator built using the U-Net architecture model and a discriminator that provides a probability matrix for each prediction, the elements of the matrix corresponding to portions of the input image. The resulting model, implemented with GPU processors provided by Google, performs a binary segmentation of images to determine the areas containing crops. The results of five experiments obtained, in the best configuration of hyper-parameters tested, an average accuracy of 97.93% in relation to reference (manual) segmentation.

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Acknowledgements

This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number 202/2020, within PNCDI III.

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Authors and Affiliations

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

    Dan Popescu, Loretta Ichim & Octavian Andrei Sava

Authors
  1. Dan Popescu
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  2. Loretta Ichim
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  3. Octavian Andrei Sava
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Corresponding author

Correspondence to Dan Popescu .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Popescu, D., Ichim, L., Sava, O.A. (2021). Semantic Segmentation of Small Region of Interest for Agricultural Research Applications. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_44

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-88081-1

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