Artifcial neural networks in online semiautomated pest discriminability: an applied case with 2 Trips species

Artifcial neural networks in online semiautomated pest discriminability: an applied case with 2 Trips species

Being faced with practical problems in pest identifcation, we present a methodical paper based on artifcial neural networksto discriminate morphologically very similar species, Trips sambuci Heeger, 1854 and Trips fuscipennis Haliday, 1836 (Tysanoptera:Tripinae), as an applied case for more general use. Te artifcially intelligent system may be successfully applied as a credible, online,semiautomated identifcation tool that extracts hidden information from noisy data, even when the standard characters have muchoverlap and the common morphological keys hint at the practical problem of high morphological plasticity. Statistical analysis of 17characters, measured or determined for each Trips fuscipennis and T. sambuci specimen (reared from larvae in our laboratories),including 15 quantitative morphometric variables, was performed to elucidate morphological plasticity, detect eventual outliers, andvisualize diferences between the studied taxa. Te computational strategy applied in this study includes a set of statistical tools (factoranalysis, correlation analysis, principal component analysis, and linear discriminant analysis) followed by the application of a multilayerperceptron artifcial neural network system, which models functions of almost arbitrary complexity. Tis complex approach has proventhe existence of 2 separate species: T. fuscipennis and T. sambuci. All the specimens could be clearly distinguished with 2 distinctsubgroups for each species, determined by sex. In conclusion, the use of an optimal 3-layer ANN architecture (17, 4, 1) enables fast andreliable 100% classifcation as proven during the extensive verifcation process.

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  • Aldrich BT, Magirang EB, Dowell FE, Kambhampati S (2007). Identifcation of termite species and subspecies of the genus Zootermopsis using near-infrared refectance spectroscopy. J Insect Sci 7: 18.
  • Ananthakrishnan TN (2005). Perspectives and dimensions of phenotypic plasticity in insects. In: Ananthakrishnan TN, Whitman D, editors. Insect Phenotypic Plasticity: Diversity of Responses. Enfeld, NH, USA: Science Publishers, pp. 1–23.
  • Apuan DA, Torres MAJ, Demayo CG (2010). Describing variations and taxonomic status of earthworms collected from selected areas in Misamis Oriental, Philippines using principal component and parsimony analysis. Egypt Acad J Biolog Sci B Zoology 2: 27–36.
  • Bilgili M (2011). Te use of artifcial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey. Turk J Agric For 35: 83–93.
  • Brunner PC, Fleming C, Frey JE (2002). A molecular identifcation key for economically important thrips species (Tysanoptera: Tripidae) using direct sequencing and a PCR-RFLP-based approach. Agr Forest Entomol 4: 127–136.
  • Chiapella J (2000). Te Deschampsia cespitosa complex in central and northern Europe: morphological analysis. Bot J Linn Soc 134: 495–512.
  • Clark JY (2003). Artifcial neural networks for species identifcation by taxonomists. Biosystems 72: 131–147.
  • Do MT, Harp JM, Norris KC (1999). A test of a pattern recognition system for identifcation of spiders. B Entomol Res 89: 217–224.
  • Esteban LG, Fernández FG, de Palacios P, Romero RM, Cano NN (2009). Artifcial neural networks in wood identifcation: the case of two Juniperus species from the Canary islands. IAWA J 30: 87–94.
  • Fausett L (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications. New York, NY, USA: Prentice Hall.
  • Fedor P, Malenovský I, Vaňhara J, Sierka W, Havel J (2008). Trips (Tysanoptera) identifcation using artifcial neural networks. B Entomol Res 98: 437–447.
  • Fedor P, Vaňhara J, Havel J, Malenovský I, Spellerberg I (2009). Artifcial intelligence in pest insect monitoring. Syst Entomol 34: 398–400.
  • Frantz G, Mellinger HC (1997). THRIPS, a computerized knowledgebase for the identifcation and management of Trips infesting vegetables in the United States. Proc Fla State Hort Soc 128: 1–4.
  • Freeman JA, Skapura DM (1992). Neural Network: Algorithm, Applications, and Programming Techniques. Reading, MA, USA: Addison-Wesley.
  • Gaston KJ, O’Neill MA (2004). Automated species identifcation: why not? Philos T Roy Soc B 359: 655–667.
  • Han R, He Y, Liu F (2012). Feasibility study on a portable feld pest classifcation system design based on DSP and 3G wireless communication technology. Sensors 12: 3118–3130.
  • Haralabous J, Georgakarakos S (1996). Artifcial neural networks as a tool for species identifcation of fsh schools. ICES J Mar Sci 53: 173–180.
  • Haykin S (1994). Neural Networks - A Comprehensive Foundation. New York, NY, USA: Macmillan College Publishing Company.
  • Henneberry TJ, Taylor EA, Smith FF (1961). Foliage and soil treatments for control of fower thrips in outdoor roses. J Econ Entomol 54: 233–235.
  • Isasi P, Galván IM (2004). Redes Neuronales Artifciales, un Enfoque Práctico. Madrid, Spain: Pearson Educación S.A. (in Spanish).
  • Jacobson RJ (1995). IPM in cucumbers – prevention of establishment of Frankliniella occidentalis. Mededelingen van de Faculteit van de Landbouwwntenschappen 60: 857–863.
  • Kavdır İ (2004). Discrimination of sunfower, weed and soil by artifcial neural networks. Comput Electron Agr 44: 153–160.
  • Kucharczyk H (2010). Comparative Morphology of the Second Larval Instar of the Trips Genus Species (Tysanoptera: Tripidae) Occurring in Poland. Olsztyn, Poland: Mantis.
  • Kucharczyk H, Kucharczyk M (2009). Trips atratus Haliday, 1836 and Trips montanus Priesner, 1920 (Tysanoptera: Tripidae) – one or two species? Comparative morphological studies. Acta Zool Acad Sci H 55: 349–364.
  • Kucharczyk H, Kucharczyk M, Stanislawek K, Fedor P (2012). Application of PCA in taxonomy research – Trips (Insecta, Tysanoptera) as a model group. In: Sanguansat P, editor. Principal Component Analysis - Multidisciplinary Applications. New York, NY, USA: InTech, pp. 111–126.
  • Lilburn TG Garrity GM (2004). Exploring prokaryotic taxonomy. Int J Sys Evol Micr 54: 7–13.
  • Marini F, Zupan J, Magri A (2004). On the use of counterpropagation artifcial neural networks to characterize Italian rice varieties. Anal Chim Acta 510: 231–240.
  • Mayo M, Watson AT (2007). Automatic species identifcation of live moths. Knowl-Based Sys 20: 195–202.
  • Mehle N, Trdan S (2012). Traditional and modern methods for the identifcation of thrips (Tysanoptera) species. J Pest Sci 85: 179–190.
  • Moore A, Miller RH (2002). Automated identifcation of optically sensed aphid (Homoptera: Aphidae) wingbeat waveforms. Ann Entomol Soc Am 95: 1–8.
  • Moritz G, Delker C, Paulsen M, Mound LA, Burgermeister W (2000). Modern methods for identifcation of Tysanoptera. EPPO Bulletin 30: 591–593.
  • Moritz G, Morris DC, Mound LA (2001). TripsID – Pest Trips of the World. CD ROM. Melbourne, Australia: ACIAR, CSIRO Publishing.
  • Moritz G, Mound LA, Morris DC, Goldarazena A (2004). Pest Trips of the World, Visual and Molecular Identifcation of Pest Trips. CD ROM. Brisbane, Australia: Centre for Biological Information Technology.
  • Mound LA (2005). Fighting, fight and fecundity: behavioural determinants of Tysanoptera structural diversity. In: Ananthakrishnan TN, Whitman D, editors. Insect Phenotypic Plasticity: Diversity of Responses. Enfeld, NH, USA: Science Publishers, pp. 81–105.
  • Mound LA (2010). Species of the genus Trips (Tysanoptera, Tripidae) from the Afro-tropical Region. Zootaxa 2423: 1–24.
  • Mound LA, Azidah AA (2009). Species of the genus Trips (Tysanoptera) from Peninsular Malaysia, with a checklist of recorded Tripidae. Zootaxa 2023: 55–68.
  • Mound LA, Kibby G (1998). Tysanoptera: An Identifcation Guide. Wallingford, UK: CAB International.
  • Mound LA, Masumoto M (2005). Te genus Trips (Tysanoptera, Tripidae) in Australia, New Caledonia and New Zealand. Zootaxa 1020: 1–64.
  • Mound LA, Minaei K (2010). Taxonomic problems in character state interpretation: variation in the wheat thrips Haplothrips tritici (Kurdjumov) (Tysanoptera, Phlaeothripidae) in Iran. Deut Entomol Z 57: 233–241.
  • Muráriková N, Vaňhara J, Tóthová A, Havel J (2011). Polyphasic approach applying artifcial neural networks, molecular analysis and postabdomen morphology to West Palaearctic Tachina spp. (Diptera, Tachinidae). B Entomol Res 101: 165– 175.
  • Nakahara S (1994). Te Genus Trips Linnaeus (Tysanoptera: Tripidae) of the New World. Technical Bulletin 1822. Washington, DC, USA: United States Department of Agriculture.
  • Patterson D (1996). Artifcial Neural Networks: Teory and Applications. Singapore: Prentice Hall.
  • Priesner H (1964). Ordnung Tysanoptera (Fransenfügler, Tripse). Berlin, Germany: Akademie-Verlag (in German).
  • Ripley BD (1993). Statistical aspects of neural networks. In: Barndof- Nielsen OE, Cox DR, Jensen JL, Kendall WS, editors. Chaos and Networks – Statistical and Probabilistic Aspects. London, UK: Chapman and Hall, pp. 40–123.
  • Rugman-Jones PF, Hoddle MS, Mound LA, Stouthamer R (2006). Molecular identifcation key for pest species of Scirtothrips (Tysanoptera: Tripidae). J Econ Entomol 99: 1813–1819.
  • Schliephake G (2001). Verzeichnis der Tysanoptera (Fransenfügler) – Physopoda (Blasenfüße) – Trips Deutschlands. Entomof Germ 5: 91–106 (in German).
  • Schliephake G, Klimt K (1979). Tysanoptera. Die Tierwelt Deutschlands 66. Jena, Germany: G. Fisher Verlag (in German).
  • Schuetz I, Breitenstein A, Moritz G (2010). Computer-based identifcation key for pest thrips and tospoviruses by use of LucID 3.4, ITS-RFLP and low-density BioChip technology. In: Persley D, Wilson C, Tomas J, Sharman M, Tree D, editors. Proceedings of the IXth International Symposium on Tysanoptera and Tospoviruses, 31 August – 4 September, 2009. J Insect Sci 10: 1–58.
  • Solis-Sanchez LO, Castañeda-Miranda R, García-Escalante JJ, Torres-Pacheco I, Guevara-González RG, Castañeda-Miranda CL, Alaniz-Lumbreras PD (2001). Scale invariant feature approach for insect monitoring. Comp Electron Agr 75: 92–99.
  • Taylor M (2010). Latest lucid identifcation tools. In: Persley D, Wilson C, Tomas J, Sharman M, Tree D, editors. Proceedings of the IXth International Symposium on Tysanoptera and Tospoviruses, 31 August – 4 September, 2009. J Insect Sci 10: 1–58.
  • Toda S, Komazaki S (2002). Identifcation of thrips species (Tysanoptera: Tripidae) on Japanese fruit trees by polymerase chain reaction and restriction fragment length polymorphism of the ribosomal ITS2 region. B Entomol Res 92: 359–363.
  • Tohidi M, Sadeghi M, Mousavi SR, Mireei SA (2012). Artifcial neural network modeling of process and product indices in deep bed drying of rough rice. Turk J Agric For 36: 738–748.
  • Vaňhara J, Murárikova N, Malenovský I, Havel J (2007). Artifcial neural networks for fy identifcation: a case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biologia 62: 462–469.
  • Vierbergen G, Kucharczyk H, Kirk WDJ (2010). A key to the second instar larvae of the Tripidae of the Western Palaearctic region (Tysanoptera). Tijdschr Entomol 153: 99–160.
  • Weeks PJD, Gaston KJ (1997). Image analysis, neural networks, and the taxonomic impediment to biodiversity studies. Biodivers Conserv 6: 263–274.
  • Zhang WJ, Barrion AT (2006). Function approximation and documentation of sampling data using artifcial neural networks. Environ Monit Assessment 122: 185–201.
  • zur Strassen R (2003). Die terebranten Tysanopteren Europas und des Mittelmeer-Gebietes. Die Tierwelt Deutschlands 74: 1–271 (in German).
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