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• W2887072651 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." @default.
• W2887072651 created "2018-08-22" @default.
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• W2887072651 date "2018-08-09" @default.
• W2887072651 modified "2023-10-14" @default.
• W2887072651 title "Unsupervised neural classification of six chosen apple pests using learned vector quantization agorithm" @default.
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• W2887072651 doi "https://doi.org/10.1117/12.2503105" @default.
• W2887072651 hasPublicationYear "2018" @default.
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