Artificial intelligence in pest insect monitoring |
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| Authors: | PETER FEDOR JAROMÍR VA?HARA JOSEF HAVEL IGOR MALENOVSKÝ IAN SPELLERBERG |
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| Institution: | 1. Department of Ecosozology, Faculty of Natural Sciences, Comenius University, Bratislava, Slovak Republic;2. Department of Botany and Zoology and Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic;3. Department of Entomology, Moravian Museum, Brno, Czech Republic;4. Isaac Centre for Nature Conservation, Lincoln University, Canterbury, New Zealand |
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| Abstract: | Abstract 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. |
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