Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-13T02:04:41.045Z Has data issue: false hasContentIssue false
  • English
  • Français

Thrips (Thysanoptera) identification using artificial neural networks

Published online by Cambridge University Press:  21 April 2008

P. Fedor ,
I. Malenovský ,
J. Vaňhara ,
W. Sierka  and
J. Havel
P. Fedor
Affiliation:
Comenius University, Faculty of Natural Sciences, Department of Ecosozology, Mlynská dolina, 842 15 Bratislava, Slovak Republic
I. Malenovský
Affiliation:
Moravian Museum, Department of Entomology, Hviezdoslavova 29a, 627 00 Brno, Czech Republic
J. Vaňhara*
Affiliation:
Masaryk University, Faculty of Science, Department of Botany and Zoology, Kotlářská 2, 611 37 Brno, Czech Republic
W. Sierka
Affiliation:
University of Silesia, Faculty of Biology and Environmental Protection, Department of Zoology, Bankowa 9, 400 07 Katowice, Poland
J. Havel
Affiliation:
Masaryk University, Faculty of Science, Department of Chemistry, Kotlářská 2, 611 37 Brno, Czech Republic
*
*Author for correspondence E-mail: vanhara@sci.muni.cz
Get access Rights & Permissions [Opens in a new window]

Abstract

We studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.

Keywords

artificial neural networksroutine identificationThysanopteraAeolothripsChirothripsDendrothripsLimothrips
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 98 , Issue 5 , October 2008 , pp. 437 - 447
Copyright
Copyright © 2008 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aldrich, B.T., Magirang, E.B., Dowell, F.E. & Kambhampati, S. (2007) Identification of termite species and subspecies of the genus Zootermopsis using near-infrared reflectance spectroscopy. Journal of Insect Science 7, 18. Available online: insectscience.org/7.18 (accessed 25 October 2007).CrossRefGoogle ScholarPubMed
Bishop, C. (1995) Neural Networks for Pattern Recognition. 504 pp. New York, Oxford University Press.CrossRefGoogle Scholar
Brunner, P.C., Fleming, C. & Frey, J.E. (2002) A molecular identification key for economically important thrips species (Thysanoptera: Thripidae) using direct sequencing and a PCR-RFLP-based approach. Agricultural and Forest Entomology 4, 127–136.CrossRefGoogle Scholar
Clark, J.Y. (2003) Artificial neural networks for species identification by taxonomists. BioSystems 72, 131–147.CrossRefGoogle ScholarPubMed
Chesmore, D. (1999) Technology transfer: applications of electronic technology in ecology and entomology for species identification. Natural History Research 5, 111–126.Google Scholar
Chesmore, D. (2004) Automated bioacoustic identification of species. Anais da Academia Brasileira de Cięncias 76, 435–440.Google ScholarPubMed
Cranston, P.S. (2005) Ancient and modern – toolboxes for e-bugs. Systematic Entomology 30, 183–185.CrossRefGoogle Scholar
Do, M.T., Harp, J.M. & Norris, K.C. (1999) A test of a pattern recognition system for identification of spiders. Bulletin of Entomological Research 89, 217–224.CrossRefGoogle Scholar
Edwards, M. & Morse, D.R. (1995) The potential for computer-aided identification in biodiversity research. Trends in Ecology and Evolution 10, 153–158.CrossRefGoogle ScholarPubMed
Fausett, L. (1994) Fundamentals of Neural Networks: Architectures, Algorithms and Applications. 461 pp. New York, Prentice Hall.Google Scholar
Fedor, P.J., Pelikán, J., Cyprich, D. & Krumpál, M. (2001) Thrips (Thysanoptera) in the nests of birds and mammals of the NPR Jurský Šúr. Folia Faunistica Slovaca 6, 69–73.Google Scholar
Franssen, C.J.H. & Mantel, W.P. (1965) Thripsen in graangewassen (levenswijze, economische betekenis en bestrijding) I. Levenswijze (Thrips in cereal crops (biology, economic importance and control) I. Biology). Instituut voor Plantenziektenkundige Onderzoek, Wageningen, Mededeling No. 381.Google Scholar
Gaston, K.J. & May, R.M. (1992) Taxonomy of taxonomists. Nature 356, 281.CrossRefGoogle Scholar
Gaston, K.J. & O'Neill, M.A. (2004) Automated species identification: why not? Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 359, 655–667.CrossRefGoogle ScholarPubMed
Hernández-Borges, J., Corbella-Tena, R., Rodriguez-Delgado, M.A., Garcia-Montelongo, F.J. & Havel, J. (2004) Content of aliphatic hydrocarbons in limpets as a new way for classification of species using Artificial Neural Networks. Chemosphere 54, 1059–1069.CrossRefGoogle ScholarPubMed
Houle, D., Mezey, J., Galpern, P. & Carter, A. (2003) Automated measurement of Drosophila wings. BMC Evolutionary Biology 3, 25. http://www.biomedcentral.com/1471-2148/3/25 (accessed 2 April 2007).CrossRefGoogle ScholarPubMed
Jones, D.R. (2005) Plant viruses transmitted by thrips. European Journal of Plant Pathology 113, 119–157.CrossRefGoogle Scholar
Larsson, H. (2005) Economic damage by Limothrips denticornis in rye, triticale and winter barley. Journal of Applied Entomology 129, 386–392.CrossRefGoogle Scholar
Lewis, T. (1973) Thrips: Their Biology, Ecology and Economic Importance. 349 pp. London, New York, Academic Press.Google Scholar
Lewis, T. (Ed.) (1997) Thrips as Crop Pests. 740 pp. Oxford and New York, CAB International.CrossRefGoogle Scholar
Marcondes, C.B. & Borges, P.S. (2000) Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an Artificial Neural Network (Diptera: Psychodidae, Phlebotominae). Memorias del Instituto de Oswaldo Cruz 95, 685–688.CrossRefGoogle ScholarPubMed
Moore, A. & Miller, R.H. (2002) Automated identification of optically sensed aphid (Homoptera: Aphidae) wingbeat waveforms. Annals of the Entomological Society of America 95, 1–8.CrossRefGoogle Scholar
Moritz, G., Delker, C., Paulsen, M., Mound, L.A. & Burgermeister, W. (2000) Modern methods for identification of Thysanoptera. EPPO Bulletin 30, 591–593.CrossRefGoogle Scholar
Moritz, G., Morris, D.C. & Mound, L.A. (2001) ThripsID – pest thrips of the world. CD ROM. ACIAR, CSIRO Publishing.Google Scholar
Moritz, G., Mound, L.A., Morris, D.C. & Goldarazena, A. (2004) Pest thrips of the world, visual and molecular identification of pest thrips. CD ROM. Center for Biological Information Technology AUD, Lucid, University of Queensland.Google Scholar
Mound, L.A. (2001) So many thrips – so few tospoviruses? pp. 3–6in Marullo, R. & Mound, L.A. (Eds) Thrips and Tospoviruses: Proceedings of the 7th International Symposium on Thysanoptera. http://www.ento.csiro.au/thysanoptera/symposium.html) (accessed 15 February 2007).Google Scholar
Mound, L.A. & Kibby, G. (1998) Thysanoptera: An Identification Guide. 2nd edn.70 pp. Oxford and New York, CAB International.Google Scholar
Patterson, D. (1996) Artificial Neural Networks: Theory and Applications. 506 pp. Singapore, Prentice Hall.Google Scholar
Pelikán, J., Fedor, P., Krumpál, M. & Cyprich, D. (2002) Thrips (Thysanoptera) in nests of birds and mammals in Slovakia. Ekológia (Bratislava) 21, 275–282.Google Scholar
Platnick, N.I., Russell, K.N. & Do, M.T. (2005) SPIDA-web. Species Identified Automatically. A neural network based automated identification system for biological species. https://research.amnh.org/invertzoo/spida/common/index.htm (accessed 15 February 2007).Google Scholar
Rugman-Jones, P.F., Hoddle, M.S., Mound, L.A. & Stouthamer, R. (2006) Molecular identification key for pest species of Scirtothrips (Thysanoptera: Thripidae). Journal of Economic Entomology 99, 1813–1819.CrossRefGoogle ScholarPubMed
Schliephake, G. & Klimt, K. (1979) Thysanoptera. Die Tierwelt Deutschlands 66. 477 pp. Jena, G. Fisher Verlag.Google Scholar
StatSoft, Inc. (2004) STATISTICA 7.0. Computer program.Google Scholar
Toda, S. & Komazaki, S. (2002) Identification of thrips species (Thysanoptera: Thripidae) on Japanese fruit trees by polymerase chain reaction and restriction fragment length polymorphism of the ribosomal ITS2 region. Bulletin of Entomological Research 92, 359–363.CrossRefGoogle ScholarPubMed
Tofilski, A. (2004) DrawWing, a program for numerical description of insect wings. Journal of Insect Science 4, 17. Available online: insectscience.org/4.17 (accessed 25 October 2007).CrossRefGoogle ScholarPubMed
Trajan Software, Ltd (1996–1998) Trajan Neural Network Simulator, version 3.0D. Computer program.Google Scholar
Vaňhara, J., Muráriková, N., Malenovský, I. & Havel, J. (2007) Artificial Neural Networks for fly identification: a case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biologia 62, 462–469.CrossRefGoogle Scholar
Weeks, P.J.D. & Gaston, K.J. (1997) Image analysis, neural networks, and the taxonomic impediment to biodiversity studies. Biodiversity and Conservation 6, 263–274.CrossRefGoogle Scholar
Weeks, P.J.D., Gauld, I.D., Gaston, K.J. & O'Neill, M.A. (1997) Automating the identification of insects: a new solution to an old problem. Bulletin of Entomological Research 87, 203–211.CrossRefGoogle Scholar
Weeks, P.J.D., O'Neill, M.A., Gaston, K.J. & Gauld, I.D. (1999) Automating insect identification: exploring the limitations of a prototype system. Journal of Applied Entomology 123, 1–8.CrossRefGoogle Scholar
zur Strassen, R. (2003) Die terebranten Thysanopteren Europas und des Mittelmeer-Gebietes. 277 pp. Keltern, Goecke and Evers.Google Scholar
zur Strassen, R. (2005) Thysanoptera. Fauna Europaea, version 1.2. http://www.faunaeur.org (accessed 9 March 2007).Google Scholar