Title: Automatic plant recognition system for challenging natural plant species
Authors: Kazerouni, Masoud Fathi
Schlemper, Jens
Kuhnert, Klaus-Dieter
Citation: WSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 81-90.
Issue Date: 2017
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
ISBN: 978-80-86943-45-9
ISSN: 2464-4617
Keywords: komponent;SURF;kombinace;FAST;HARRIS;extrakce vlastností;detekce funkcí;přirozené obrazy;přirozené rozpoznání druhů rostlin;povětrnostní podmínky;intenzita světla
Keywords in different language: component;SURF;combination;FAST;HARRIS;feature extraction;feature detection;natural images;natural plant species recognition;weather condition;light intensity
Abstract: Photosynthesis is one of turning points to shape the world. Plants use this process to convert light energy into chemical energy. Some of the early microorganisms evolved a way to use the energy from sunlight to make sugar out of simpler molecules, but unlike green plants today, the first photosynthesizing organisms did not release oxygen as waste product, so there was no oxygen in the air. Plants are very busy factories and leaves are the main place for production. A useful plant recognition system is capable of identification of different species in natural environment. In natural environment, plants and leaves grow in different regions and climates. During day, variation of light intensity can be considered as an important factor. Thus, recognition of species in different conditions is a real need as plants are ubiquitous in human life. A dataset of natural images has been utilized. The dataset contains four different plant species of Siegerland, Germany. Modern combined description algorithms, SURF, FAST-SURF, and HARRIS-SURF, have been carried out to implement a reliable system for plants species recognition and classification in natural environment. One of well known methods in machine learning community, Support Vector Machine, has been applied in the implemented systems. All steps of system’s implementation are described in related sections. The highest obtained accuracy belongs to the implemented system by means of SURF algorithm and equals to 93.9575.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2017: Short Papers Proceedings

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