Artificial intelligence in pest insect monitoring
| Autor: | Peter Fedor, Igor Malenovský, Josef Havel, Ian F. Spellerberg, Jaromír Vaňhara |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
2. Zero hunger
0106 biological sciences Thrips biology Artificial neural network business.industry biology.organism_classification Perceptron 010603 evolutionary biology 01 natural sciences Set (abstract data type) 010602 entomology Identification (information) Insect Science Ovipositor Artificial intelligence PEST analysis business Ecology Evolution Behavior and Systematics Test data |
| Zdroj: | Systematic Entomology. 34:398-400 |
| ISSN: | 0307-6970 |
| DOI: | 10.1111/j.1365-3113.2008.00461.x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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