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Unsupervised neural classification of six chosen apple pests using learned vector quantization agorithm
Author(s): P. Boniecki; H. Piekarska-Boniecka; K. Przybył; Ł. Gierz; K. Koszela; M. Zaborowicz; D. Lisiak; P. Ślósarz; J. Przybył
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Paper Abstract

The aim of this work was a neural identification of selected apple tree orchard pests in Poland. The classification was conducted on the basis of graphical information coded in the form of selected geometric characteristics of agrofags, presented on digital images. A neural classification model is presented in this paper, optimized using learning files acquired on the basis of information contained in digital photographs of pests. There has been identified 6 selected apple pests, the most commonly encountered in Polish orchards, has been addressed. In order to classify the chosen agrofags, neural networks type Self-Organizing Feature Map (SOFM) methods supported Learned Vector Quantization (LVQ) algorithm were utilized, using by digital analysis of image techniques.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108066V (9 August 2018); doi: 10.1117/12.2503105
Show Author Affiliations
P. Boniecki, Poznan Univ. of Life Sciences (Poland)
H. Piekarska-Boniecka, Poznan Univ. of Life Sciences (Poland)
K. Przybył, Poznan Univ. of Life Sciences (Poland)
Ł. Gierz, Poznan Univ. of Technology (Poland)
K. Koszela, Poznan Univ. of Life Sciences (Poland)
M. Zaborowicz, Poznan Univ. of Life Sciences (Poland)
D. Lisiak, Institute of Agricultural and Food Biotechnology (Poland)
P. Ślósarz, Poznan Univ. of Life Sciences (Poland)
J. Przybył, Poznan Univ. of Life Sciences (Poland)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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