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9 August 2018 Unsupervised neural classification of six chosen apple pests using learned vector quantization agorithm
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108066V (2018) https://doi.org/10.1117/12.2503105
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
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.
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P. Boniecki, H. Piekarska-Boniecka, K. Przybył, Ł. Gierz, K. Koszela, M. Zaborowicz, D. Lisiak, P. Ślósarz, and J. Przybył "Unsupervised neural classification of six chosen apple pests using learned vector quantization agorithm", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108066V (9 August 2018); https://doi.org/10.1117/12.2503105
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