Published March 12, 2021 | Version v1
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Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

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Knyshov, Alexander, Hoang, Samantha, Weirauch, Christiane (2021): Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images. Insect Systematics and Diversity (AIFB) 5 (2), No. 3: 1-10, DOI: 10.1093/isd/ixab004, URL: http://dx.doi.org/10.1093/isd/ixab004

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  • Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. 2016. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Preprint at https://arxiv. org/abs/1603.04467
  • Bisgin, H., T. Bera, H. Ding, H. G. Semey, L. Wu, Z. Liu, A. E. Barnes, D. A. Langley, M. Pava-Ripoll, H. J. Vyas, et al. 2018. Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles. Sci. Rep. 8: 1-12.
  • Buschbacher, K., D. Ahrens, M. Espeland, and V. Steinhage. 2020. Imagebased species identification of wild bees using convolutional neural networks. Ecol. Inform. 55: 101017.
  • de Carvalho, M. R., F. A. Bockmann, D. S. Amorim, C. R. F. Brandao, M. de Vivo, J. L. de Figueiredo, H. A. Britski, M. C. C. de Pinna, N. A. Menezes, F. P. L. Marques, et al. 2007. Taxonomic impediment or impediment to taxonomy? A commentary on systematics and the cybertaxonomic-automation paradigm. Evol. Biol. 34: 140-143.
  • Cassis, G., and R. T. Schuh. 2012. Systematics, biodiversity, biogeography, and host associations of the Miridae (Insecta: Hemiptera: Heteroptera: Cimicomorpha). Annu. Rev. Entomol. 57: 377-404.
  • Chollet, F. 2015. Keras. (https://keras.io/).
  • Chulu, F., Phiri, J., Nkunika, P. O., Nyirenda, M., Kabemba, M. M. and P. H. Sohati. 2019. A convolutional neural network for automatic identification and classification of fall army worm moth. Int. J. Adv. Comput. Sci. Applic. 10: 112-118.
  • Cortes, C., and V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20: 273-297.
  • Ferreira, P. S. F., T. J. Henry, and L. A. Coelho. 2015. Plant bugs (Miridae), pp. 237-286. In True Bugs Neotrop. Springer, Netherlands.
  • Gaston, K. J., and M. A. O'Neill. 2004. Automated species identification: why not? Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 359: 655-667.
  • Hansen, O. L. P., J. C. Svenning, K. Olsen, S. Dupont, B. H. Garner, A. Iosifidis, B. W. Price, and T. T. HOye. 2020. Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecol. Evol. 10: 737-747.
  • Henry, T. J. 2018. Revision of the plant bug genus Semium (Heteroptera: Miridae: Phylinae: Semiini), with the description of three new species and a revised key. Proc. Entomol. Soc. Washingt. 120: 508-532.
  • Hopkins, G. W., and R. P. Freckleton. 2002. Declines in the numbers of amateur and professional taxonomists: implications for conservation. Anim. Conserv. 5: 245-249.
  • Horn, G. Van, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie. 2018. The iNaturalist species classification and detection dataset, pp. 8769-8778. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-22 June 2018, Salt Lake City, UT. IEEE, Piscataway, NJ.
  • Hosseini, R., and G. Cassis. 2019. Systematics of the plant bug tribe Hyaliodini (Hemiptera, Heteroptera, Miridae, Deraeocorinae) from Australia and New Caledonia: phylogenetic analysis and discussion of deraeocorine relationships, and four new genera and thirteen new species.Insect Syst. Evol. 50: 445-582.
  • Hudson, L. N., V. Blagoderov, A. Heaton, P. Holtzhausen, L. Livermore, B. W. Price, S. Van Der Walt, and V. S. Smith. 2015. Inselect: automating the digitization of natural history collections. PLoS One 10: e0143402.
  • Jung, S., and S. Lee. 2012. Molecular phylogeny of the plant bugs (Heteroptera: Miridae) and the evolution of feeding habits. Cladistics 28: 50-79.
  • Kim, J., and S. Jung. 2019. Phylogeny of the plant bug subfamily Mirinae (Hemiptera: Heteroptera: Cimicomorpha: Miridae) based on total evidence analysis. Syst. Entomol. 44: 686-698.
  • Knyshov, A., and F. V. Konstantinov. 2013. A taxonomic revision of the genus Platycranus Fieber, 1870 (Hemiptera: Heteroptera: Miridae: Orthotylinae). Zootaxa. 3637: 201-253.
  • Knyshov, A., E. R. L. Gordon, and C. Weirauch. 2019. Cost-efficient high throughput capture of museum arthropod specimen DNA using PCRgenerated baits. Methods Ecol. Evol. 10: 841-852.
  • Larios, N., H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, S. R. Correa, E. N. Mortensen, et al. 2008. Automated insect identification through concatenated histograms of local appearance features: Feature vector generation and region detection for deformable objects. Mach. Vis. Appl. 19: 105-123.
  • Mantle, B. L., J. la Salle, and N. Fisher. 2012. Whole-drawer imaging for digital management and curation of a large entomological collection. Zookeys. 209: 147-163.
  • Marques, A. C. R., M. M. Raimundo, E. M. B. Cavalheiro, L. F. P. Salles, C. Lyra, and F. J. Von Zuben. 2018. Ant genera identification using an ensemble of convolutional neural networks. PLoS One 13: e0192011.
  • Martineau, M., D. Conte, R. Raveaux, I. Arnault, D. Munier, and G. Venturini. 2017. A survey on image-based insect classification. Pattern Recognit. 65: 273-284.
  • Mayo, M., and A. T. Watson. 2007. Automatic species identification of live moths. Knowledge-Based Syst. 20: 195-202.
  • Menard, K. L., R. T. Schuh, and J. B. Woolley. 2014. Total-evidence phylogenetic analysis and reclassification of the Phylinae (Insecta: Heteroptera: Miridae), with the recognition of new tribes and subtribes and a redefinition of Phylini. Cladistics. 30: 391-427.
  • Namyatova, A. A., F. V. Konstantinov, and G. Cassis. 2016. Phylogeny and systematics of the subfamily Bryocorinae based on morphology with emphasis on the tribe Dicyphini sensu Schuh, 1976. Syst. Entomol. 41: 3-40.
  • Pedregosa, F., V. Michel, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, J. Vanderplas, D. Cournapeau, F. Pedregosa, G. Varoquaux, et al. 2011. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12: 2825-2830.
  • Schuh, R. T. 2004. Revision of Tuxedo Schuh (Hemiptera: Miridae: Phylinae). Am. Museum Novit. 3435: 1.
  • Schuh, R. T., and C. Weirauch. 2020. True bugs of the world (Hemiptera, Heteroptera) classification and natural history, 2nd ed. Siri Scientific Press, Rochdale.
  • Schwartz, M. D., and R. T. Schuh. 2016. Two new species of Pulvillophylus from Western Australia (Insecta: Hemiptera: Miridae: Phylinae: Cremnorrhinini).Am. Museum Novit. 3860: 1-12.
  • Schwartz, M. D., C. Weirauch, and R. T. Schuh. 2018. New genera and species of Myrtaceae-feeding Phylinae from Australia, and the description of a new species of Restiophylus (Insecta: Heteroptera: Miridae). Bull. Am. Museum Nat. Hist. 424: 162.
  • Simonyan, K., and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In 3rd Int. Conf. Learn. Represent, 7-9 May 2015, San Diego, CA. arXiv, Ithaca, NY.
  • Stanitsas,P.,A.Cherian,A.Truskinovsky,V.Morellas,and N.Papanikolopoulos. 2018. Active convolutional neural networks for cancerous tissue recognition, pp. 1367-1371. In Proc. - Int. Conf. Image Process, 17-20 September 2017, Beijing, China. IEEE, Piscataway, NJ.
  • Terry, J. C. D., Roy, H. E., and T. A. August. 2020. Thinking like a naturalist: Enhancing computer vision of citizen science images by harnessing contextual data. Methods Ecol. Evol. 11: 303-315.
  • Thenmozhi, K. and U. Srinivasulu Reddy. 2019. Crop pest classification based on deep convolutional neural network and transfer learning. Comput. Electron. Agric. 164: 104906.
  • Ubeyli, E. D. 2007. Implementing automated diagnostic systems for breast cancer detection. Expert Syst. Appl. 33: 1054-1062.
  • Valan, M., K. Makonyi, A. Maki, D. Vondracek, and F. Ronquist. 2019. Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks. Syst. Biol. 68: 876-895.
  • Waldchen, J. and P. Mader. 2018. Machine learning for image based species identification. Methods Ecol. Evol. 9: 2216-2225.
  • Weirauch, C. 2006. New genera and species of oak-associated Phylini (Heteroptera: Miridae: Phylinae) from Western North America. Am. Museum Novit. 2006: 1-54.
  • Weirauch, C. 2007. Revision and ciadistic analysis of the Polyozus group of Australian phylini (heteroptera: Miridae: Phylinae). Am. Museum Novit. 2007: 1-60.
  • Wilson, E. O. 2004. Taxonomy as a fundamental discipline. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 359: 739-739.
  • Wu, L., Liu, Z., Bera, T., Ding, H., Langley, D. A., Jenkins-Barnes, A., Furlanello, C., Maggio, V., Tong, W. and J. Xu. 2019. A deep learning model to recognize food contaminating beetle species based on elytra fragments. Comput. Electron. Agric. 166: 105002.
  • Yang, M., K. Nurzynska, A. E. Walts, and A. Gertych. 2020. A CNN-based active learning framework to identify mycobacteria in digitized Ziehl- Neelsen stained human tissues. Comput. Med. Imaging Graph. 84: 101752.