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Artificial intelligence in pest insect monitoring
Author(s) -
FEDOR PETER,
VAŇHARA JAROMÍR,
HAVEL JOSEF,
MALENOVSKÝ IGOR,
SPELLERBERG IAN
Publication year - 2009
Publication title -
systematic entomology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.552
H-Index - 66
eISSN - 1365-3113
pISSN - 0307-6970
DOI - 10.1111/j.1365-3113.2008.00461.x
Subject(s) - biology , ovipositor , pest analysis , artificial neural network , extrapolation , reliability (semiconductor) , artificial intelligence , identification (biology) , perceptron , pattern recognition (psychology) , ecology , computer science , statistics , botany , mathematics , hymenoptera , power (physics) , physics , quantum mechanics
Global problems of hunger and malnutrition induced us to introduce a new tool for semi‐automated pest insect identification and monitoring: an artificial neural network system. Multilayer perceptrons, an artificial intelligence method, seem to be efficient for this purpose. We evaluated 101 European economically important thrips (Thysanoptera) species: extrapolation of the verification test data indicated 95% reliability at least for some taxa analysed. Mainly quantitative morphometric characters, such as head, clavus, wing, ovipositor length and width, formed the input variable computation set in a Trajan neural network simulator. The technique may be combined with digital image analysis.