Unsupervised neural classification of six chosen apple pests using learned vector quantization agorithm
- 9 August 2018
- conference paper
- Published by SPIE-Intl Soc Optical Eng
- Vol. 10806, 227
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.Keywords
This publication has 24 references indexed in Scilit:
- Classification of dried vegetables using computer image analysis and artificial neural networks2017
- Computer image analysis in caryopses quality evaluation as exemplified by malting barley2015
- Neural identification of selected apple pestsComputers and Electronics in Agriculture, 2015
- Organoleptic damage classification of potatoes with the use of image analysis in production process2014
- Detection of the granary weevil based on X-ray images of damaged wheat kernelsJournal of Stored Products Research, 2014
- Identification of physical parameters of cereal grain using computer image analysis and neural models2013
- Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition2012
- Pest status of leafminers in cider-apples: The case of orchards in Asturias (NW Spain)Crop Protection, 2011
- Effect of botanically derived pesticides on mirid pests and beneficials in appleCrop Protection, 2009
- Comparison of the effectiveness of standard and drift-reducing nozzles for control of some pests of appleCrop Protection, 2005