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Detection of Grapes in Natural Environment Using Feedforward Neural Network as a Classifier

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F16%3A39902004" target="_blank" >RIV/00216275:25530/16:39902004 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Detection of Grapes in Natural Environment Using Feedforward Neural Network as a Classifier

  • Original language description

    The recognition of wine grapes in images acquired in natural environment is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red kinds is a well managed problem. On the other hand, the detection of white grapes is still a challenging task. In this contribution, the classifier for white wine grapes recognition is introduced and evaluated. The classifier is based on an artificial neural network and is used in two ways which differ in image representation. Namely, the pixel intensities and histogram of oriented gradients are used for the representation of images. Then, feedforward multilayer neural network is applied as a classifier. The classifiers based on the histograms of oriented gradients seemed to be very effective - they were almost error free from the cross validation point of view and they performed well with the independent testing data sets, too. On the other hand, the representation using pixel intensities was stated as insufficient for classification using our approach.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BD - Information theory

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of 2016 SAI Computing Conference

  • ISBN

    978-1-4673-8460-5

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    1330-1334

  • Publisher name

    IEEE (Institute of Electrical and Electronics Engineers)

  • Place of publication

    New York

  • Event location

    Londýn

  • Event date

    Jul 13, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000389451900194