Throughout 2021, to carry out a government project of creating a thematic chart of illegal cannabis plantation fields, an aerial mapping operation was carried out utilizing drones. To increase the efficiency of detecting an illegal cannabis plantation, a simple and robust image segmentation was used. Thousands of aerial images were segmented and then illegal cannabis plantation fields were detected using the thresholding method. The result was then compared to the illegal cannabis plantation field detected by experts from the Indonesian National Narcotics Agency. Based on this comparison, the algorithm proposed in this work was able to detect Illegal cannabis plantation fields with an accuracy of 65%.


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    Titel :

    Segmentation of drone-captured images for illegal cannabis plantation detection


    Beteiligte:
    Tanasa, I. Y. (Autor:in) / Hidayat, A. (Autor:in) / Wibowo, M. (Autor:in) / Budiarti, D. H. (Autor:in) / Guno, Y. (Autor:in) / Purwanto, A. (Autor:in) / Yunata, A. S. (Autor:in) / Purnamastuti, F. N. (Autor:in) / Wicaksono, G. (Autor:in) / Domiri, D. (Autor:in)

    Kongress:

    THE 9TH INTERNATIONAL SEMINAR ON AEROSPACE SCIENCE AND TECHNOLOGY – ISAST 2022 ; 2022 ; Bogor, Indonesia


    Erschienen in:

    Erscheinungsdatum :

    11.12.2023


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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