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Yıl 2021, Cilt: 9 Sayı: 4, 91 - 96, 31.12.2021

Mahmut Tokmak İsmail Şen

https://doi.org/10.18100/ijamec.989263

Öz

Kaynakça

  • T.C. Narendran, "An Introduction to Taxonomy". Zool. Surv. India, Kolkata, 2006.
  • M. Ohl, "Principles of Taxonomy and Classification: Current Procedures for Naming and Classifying Organisms" in Handbook of Paleoanthropology, W. Henke, I. Tattersall, Eds. Berlin, Heidelberg, Springer, 2015, pp. 213-236.
  • R. Sluys, "The unappreciated, fundamentally analytical nature of taxonomy and the implications for the inventory of biodiversity", Biodivers. Conservation, 22: 1095-1105, 2013.
  • K.D. Prathapan, P.D. Rajan, "Advancing taxonomy in the global south and completing the grand Linnaean enterprise" Megataxa, 1(1): 73-77, 2020.
  • P.A. Hutchings, "Major issues facing taxonomy-a personal perspective", Megataxa, 1(1), 46-48, 2020.
  • X. Cheng, YH. Zhang, YZ. Wu, Y. Yue, "Agricultural Pests Tracking and Identification in Video Surveillance Based on Deep Learning" in Intelligent Computing Methodologies, D.S. Huang, A. Hussain, K. Han, M. Gromiha Eds. Lecture Notes in Computer Science, vol 10363. Springer, Cham, 2017, pp. 58-70.
  • G. Figueroa-Mata, E. Mata-Montero, J.C. Valverde-Otárola, D. Arias-Aguilar, "Automated image-based identification of forest species: challenges and opportunities for 21st century xylotheques" in IEEE International Work Conference on Bioinspired Intelligence (IWOBI), Alajuela Province, Costa Rica, 2018, pp. 1-8.
  • T. Kasinathan, D. Singaraju, S.R. Uyyala, "Insect classification and detection in field crops using modern machine learning techniques" in Information Processing in Agriculture, 2020.
  • D.L. Saccaggi, K. Krüger, G. Pietersen, "A multiplex PCR assay for the simultaneous identification of three mealybug species (Hemiptera: Pseudococcidae)", Bull. Entomol. Res., 98: 27-33, 2008.
  • J. Blair, M.D. Weiser, M. Kaspari, M. Miller, C. Siler, K.E. Marshall, "Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale", Ecol. Evol., 10(23): 13143-13153, 2020.
  • P.D. Hebert, A. Cywinska, S.L. Ball, J.R. Dewaard, "Biological identifications through DNA barcodes" Proc. R. Soc. London, Ser. B: Biological Sciences, 270(1512), 313-321, 2003.
  • D. Dunbar, C. Nielsen, "Development of a DNA Bar-coding Project as a Biology Laboratory Module" J. Microbiol. Biol. Educ., 11(2): 160-161, 2010.
  • C.O. Coleman, A.E. Radulovici, "Challenges for the future of taxonomy: talents, databases and knowledge growth" Megataxa, 1(1): 28-34, 2020.
  • M. Vences, "The promise of next-generation taxonomy", Megataxa, 1(1), 35-38, 2020.
  • J. Wäldchen, P. Mäder, "Machine learning for image based species identification", Methods Ecol. Evol., 9(11), 2216-2225, 2018.
  • J.G.A. Barbedo, "Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review", AI, 1(2): 312-328, 2020.
  • M. Tokmak, A. Kıraç, "Evrişimsel Sinir Ağları ile Örümcek Kuşugillerin Bazı Türlerinin Sınıflandırılması" Bilge International Journal of Science and Technology Research, 5 (1): 72-79, 2021.
  • D. Grimaldi, M.S. Engel, "Evolution of the insects", Cambridge University Press, New York, USA, 2005, pp. 772.
  • S.N. Yaakob, L. Jain, "An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant", Appl. Intell., 37(1), 12-30, 2012.
  • P. Jolivet, K.K. Verma, "Biology of leaf beetles" Intercept Publishers, UK, 2002, pp. 332.
  • N. Mirzoeva, "A study of the ecofaunal complexes of the leaf-eating beetles (Coleoptera, Chrysomelidae) in Azerbaijan" Turk. J. Zool., 25: 41-52, 2001.
  • G. Magoga, D. Coral Sahin, D. Fontaneto, M. Montagna, "Barcoding of Chrysomelidae of Euro-Mediterranean area: efficiency and problematic species", Sci. Rep. 8(1): 1-9, 2018.
  • D. Coral Sahin, G. Magoga, H. Özdikmen, M. Montagna, "DNA Barcoding as useful tool to identify crop pest flea beetles of Turkey" J. Appl. Entomol., 143(1-2): 105-117, 2019.
  • www.cassidae.uni.wroc.pl/European%20Chrysomelidae/list% 20of%20subfamilies.htm
  • M. Hussain, J.J. Bird, D.R. Faria, "A Study on CNN Transfer Learning for Image Classification. Advances in Computational Intelligence Systems", in Advances in Intelligent Systems and Computing, vol 840, A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, M. McGinnity Eds. Springer, Cham., 2019, pp.191-202.
  • K. O’Shea, R. Nash, "An introduction to convolutional neural networks". arXiv preprint arXiv:1511.08458, 2015.
  • X. Bai, B. Shi, C. Zhang, X. Cai, L. Qi, "Text/non-text image classification in the wild with convolutional neural networks" Pattern Recognit., 66: 437-446, 2017.
  • D. Scherer, A. Müller, S. Behnke, "Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition" in Artificial Neural Networks – ICANN 2010, Lecture Notes in Computer Science, vol 6354, K. Diamantaras, W. Duch, L.S. Iliadis Eds. Berlin, Heidelberg, Springer, 2010, pp. 92-101.
  • R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi "Convolutional neural networks: an overview and application in radiology" Insights into Imaging, 9(4): 611-629, 2018.
  • A. Gebrehiwot, L. Hashemi-Beni, G. Thompson, P. Kordjamshidi, T.E Langan, "Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data" Sensors (Basel), 19: 1486, 2019.
  • M. Togacar, B. Ergen, M.E. Sertkaya, "Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models" Elektronika ir Elektrotechnika, 25(5), 63-68, 2019.
  • K. Simonyan, A. Zisserman, "Very deep convolutional Networks for large-scale image recognition" arXivPrepr arXiv14091556, 2014.
  • K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition" in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Las Vegas, USA, 2016, pp. 770–778.
  • G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, "Densely connected convolutional Networks" in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Honolulu, Hawaii, 2017, 4700-4708.
  • A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, H. Adam, "Mobilenets: Efficient convolutional neural networks for mobile vision applications", arXivpreprint arXiv:1704.04861, 2017.
  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks" in Proceedings of the IEEE conference on computer vision and pattern recognition 2018, pp. 4510-4520.
  • Colab. (2021). Google Colaboratory. https://colab.research. google.com/
  • fast.ai. (2021). fast.ai. https://www.fast.ai/
  • A.C.R. Marques, M.M. Raimundo, E.M.B. Cavalheiro, L.F.P. Salles, C. Lyra, F.J. von Zuben, "Ant genera identification using an ensemble of convolutional neural networks" Plos one, 13(1), e0192011, 2018.
  • O.L. Hansen, J.C. Svenning, K. Olsen, S. Dupont, B.H. Garner, A. Iosifidis, B.W. Price, T.T. Høye, "Species‐level image classification with convolutional neural network enables insect identification from habitus images" Ecol. Evol., 10(2), 737-747, 2020.
  • A. Knyshov, S. Hoang, C. Weirauch, "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 Syst. Diversity, 5(2), 3, 2021.
  • H. Theivaprakasham" Identification of Indian butterflies using Deep Convolutional Neural Network" J. Asia-Pac. Entomol., 24(1), 329-340, 2021.
  • T.T. Høye, J. Ärje, K. Bjerge, O.L.P. Hansen, A. Iosifidis, F. Leese, H.M.R. Mann, K. Meissner, C. Melvad, J. Raitoharju, "Deep learning and computer vision will transform entomology", PNAS, 118(2), e2002545117, 2021.
  • B.P. Hedrick, J.M. Heberling, E.K. Meineke, K.G. Turner, C.J. Grassa, D.S. Park, J. Kennedy, J.A. Clarke, J.A. Cook, D.C. Blackburn, S.V. Edwards, C.C. Davis, "Digitization and the Future of Natural History Collections", BioScience, 70(3): 243-251, 2020.

The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification

Yıl 2021, Cilt: 9 Sayı: 4, 91 - 96, 31.12.2021

Mahmut Tokmak İsmail Şen

https://doi.org/10.18100/ijamec.989263

Öz

Identifying an organism requires taxonomic expertise, time, and often adult specimens of that organism. Accurate identification of organisms is of great importance for sustainable agriculture, forestry and fisheries, combating pests and human diseases, disaster management, sustainable trade of biological products and management of alien invasive species. Advances in machine learning techniques have paved the way for the identification of animals by image analysis. In this context, it is aimed to test the success of different convolutional neural network (CNN) models in classifying leaf beetle (Coleoptera: Chrysomelidae) dorsal habitus images at the genus level. In this study, a total of 888 habitus images belonging to 17 genera were obtained from a website on leaf beetles and five CNN models (ResNet-152, Alex-Net, DenseNet-201, VGG-16 and MobileNet-V2) were used to classify leaf beetle genera. Also, the classification performance of the models was compared. The most successful model was ResNet-152 with an accuracy rate of 97.74%. These results showed that Resnet-152 can be used to identify European leaf beetle genera. As a result of this study, it was concluded that as the number of images increases, the identification of leaf beetles at the genus level can be made more easily by using CNNs.

Anahtar Kelimeler

insect systematic machine learning convolutional neural networks habitus image image-based identification

Kaynakça

  • T.C. Narendran, "An Introduction to Taxonomy". Zool. Surv. India, Kolkata, 2006.
  • M. Ohl, "Principles of Taxonomy and Classification: Current Procedures for Naming and Classifying Organisms" in Handbook of Paleoanthropology, W. Henke, I. Tattersall, Eds. Berlin, Heidelberg, Springer, 2015, pp. 213-236.
  • R. Sluys, "The unappreciated, fundamentally analytical nature of taxonomy and the implications for the inventory of biodiversity", Biodivers. Conservation, 22: 1095-1105, 2013.
  • K.D. Prathapan, P.D. Rajan, "Advancing taxonomy in the global south and completing the grand Linnaean enterprise" Megataxa, 1(1): 73-77, 2020.
  • P.A. Hutchings, "Major issues facing taxonomy-a personal perspective", Megataxa, 1(1), 46-48, 2020.
  • X. Cheng, YH. Zhang, YZ. Wu, Y. Yue, "Agricultural Pests Tracking and Identification in Video Surveillance Based on Deep Learning" in Intelligent Computing Methodologies, D.S. Huang, A. Hussain, K. Han, M. Gromiha Eds. Lecture Notes in Computer Science, vol 10363. Springer, Cham, 2017, pp. 58-70.
  • G. Figueroa-Mata, E. Mata-Montero, J.C. Valverde-Otárola, D. Arias-Aguilar, "Automated image-based identification of forest species: challenges and opportunities for 21st century xylotheques" in IEEE International Work Conference on Bioinspired Intelligence (IWOBI), Alajuela Province, Costa Rica, 2018, pp. 1-8.
  • T. Kasinathan, D. Singaraju, S.R. Uyyala, "Insect classification and detection in field crops using modern machine learning techniques" in Information Processing in Agriculture, 2020.
  • D.L. Saccaggi, K. Krüger, G. Pietersen, "A multiplex PCR assay for the simultaneous identification of three mealybug species (Hemiptera: Pseudococcidae)", Bull. Entomol. Res., 98: 27-33, 2008.
  • J. Blair, M.D. Weiser, M. Kaspari, M. Miller, C. Siler, K.E. Marshall, "Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale", Ecol. Evol., 10(23): 13143-13153, 2020.
  • P.D. Hebert, A. Cywinska, S.L. Ball, J.R. Dewaard, "Biological identifications through DNA barcodes" Proc. R. Soc. London, Ser. B: Biological Sciences, 270(1512), 313-321, 2003.
  • D. Dunbar, C. Nielsen, "Development of a DNA Bar-coding Project as a Biology Laboratory Module" J. Microbiol. Biol. Educ., 11(2): 160-161, 2010.
  • C.O. Coleman, A.E. Radulovici, "Challenges for the future of taxonomy: talents, databases and knowledge growth" Megataxa, 1(1): 28-34, 2020.
  • M. Vences, "The promise of next-generation taxonomy", Megataxa, 1(1), 35-38, 2020.
  • J. Wäldchen, P. Mäder, "Machine learning for image based species identification", Methods Ecol. Evol., 9(11), 2216-2225, 2018.
  • J.G.A. Barbedo, "Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review", AI, 1(2): 312-328, 2020.
  • M. Tokmak, A. Kıraç, "Evrişimsel Sinir Ağları ile Örümcek Kuşugillerin Bazı Türlerinin Sınıflandırılması" Bilge International Journal of Science and Technology Research, 5 (1): 72-79, 2021.
  • D. Grimaldi, M.S. Engel, "Evolution of the insects", Cambridge University Press, New York, USA, 2005, pp. 772.
  • S.N. Yaakob, L. Jain, "An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant", Appl. Intell., 37(1), 12-30, 2012.
  • P. Jolivet, K.K. Verma, "Biology of leaf beetles" Intercept Publishers, UK, 2002, pp. 332.
  • N. Mirzoeva, "A study of the ecofaunal complexes of the leaf-eating beetles (Coleoptera, Chrysomelidae) in Azerbaijan" Turk. J. Zool., 25: 41-52, 2001.
  • G. Magoga, D. Coral Sahin, D. Fontaneto, M. Montagna, "Barcoding of Chrysomelidae of Euro-Mediterranean area: efficiency and problematic species", Sci. Rep. 8(1): 1-9, 2018.
  • D. Coral Sahin, G. Magoga, H. Özdikmen, M. Montagna, "DNA Barcoding as useful tool to identify crop pest flea beetles of Turkey" J. Appl. Entomol., 143(1-2): 105-117, 2019.
  • www.cassidae.uni.wroc.pl/European%20Chrysomelidae/list% 20of%20subfamilies.htm
  • M. Hussain, J.J. Bird, D.R. Faria, "A Study on CNN Transfer Learning for Image Classification. Advances in Computational Intelligence Systems", in Advances in Intelligent Systems and Computing, vol 840, A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, M. McGinnity Eds. Springer, Cham., 2019, pp.191-202.
  • K. O’Shea, R. Nash, "An introduction to convolutional neural networks". arXiv preprint arXiv:1511.08458, 2015.
  • X. Bai, B. Shi, C. Zhang, X. Cai, L. Qi, "Text/non-text image classification in the wild with convolutional neural networks" Pattern Recognit., 66: 437-446, 2017.
  • D. Scherer, A. Müller, S. Behnke, "Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition" in Artificial Neural Networks – ICANN 2010, Lecture Notes in Computer Science, vol 6354, K. Diamantaras, W. Duch, L.S. Iliadis Eds. Berlin, Heidelberg, Springer, 2010, pp. 92-101.
  • R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi "Convolutional neural networks: an overview and application in radiology" Insights into Imaging, 9(4): 611-629, 2018.
  • A. Gebrehiwot, L. Hashemi-Beni, G. Thompson, P. Kordjamshidi, T.E Langan, "Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data" Sensors (Basel), 19: 1486, 2019.
  • M. Togacar, B. Ergen, M.E. Sertkaya, "Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models" Elektronika ir Elektrotechnika, 25(5), 63-68, 2019.
  • K. Simonyan, A. Zisserman, "Very deep convolutional Networks for large-scale image recognition" arXivPrepr arXiv14091556, 2014.
  • K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition" in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Las Vegas, USA, 2016, pp. 770–778.
  • G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, "Densely connected convolutional Networks" in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Honolulu, Hawaii, 2017, 4700-4708.
  • A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, H. Adam, "Mobilenets: Efficient convolutional neural networks for mobile vision applications", arXivpreprint arXiv:1704.04861, 2017.
  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks" in Proceedings of the IEEE conference on computer vision and pattern recognition 2018, pp. 4510-4520.
  • Colab. (2021). Google Colaboratory. https://colab.research. google.com/
  • fast.ai. (2021). fast.ai. https://www.fast.ai/
  • A.C.R. Marques, M.M. Raimundo, E.M.B. Cavalheiro, L.F.P. Salles, C. Lyra, F.J. von Zuben, "Ant genera identification using an ensemble of convolutional neural networks" Plos one, 13(1), e0192011, 2018.
  • O.L. Hansen, J.C. Svenning, K. Olsen, S. Dupont, B.H. Garner, A. Iosifidis, B.W. Price, T.T. Høye, "Species‐level image classification with convolutional neural network enables insect identification from habitus images" Ecol. Evol., 10(2), 737-747, 2020.
  • A. Knyshov, S. Hoang, C. Weirauch, "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 Syst. Diversity, 5(2), 3, 2021.
  • H. Theivaprakasham" Identification of Indian butterflies using Deep Convolutional Neural Network" J. Asia-Pac. Entomol., 24(1), 329-340, 2021.
  • T.T. Høye, J. Ärje, K. Bjerge, O.L.P. Hansen, A. Iosifidis, F. Leese, H.M.R. Mann, K. Meissner, C. Melvad, J. Raitoharju, "Deep learning and computer vision will transform entomology", PNAS, 118(2), e2002545117, 2021.
  • B.P. Hedrick, J.M. Heberling, E.K. Meineke, K.G. Turner, C.J. Grassa, D.S. Park, J. Kennedy, J.A. Clarke, J.A. Cook, D.C. Blackburn, S.V. Edwards, C.C. Davis, "Digitization and the Future of Natural History Collections", BioScience, 70(3): 243-251, 2020.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Mahmut Tokmak 0000-0003-0632-4308

İsmail Şen 0000-0002-9905-3537

Erken Görünüm Tarihi 29 Aralık 2021
Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 4

Kaynak Göster

APA Tokmak, M., & Şen, İ. (2021). The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification. International Journal of Applied Mathematics Electronics and Computers, 9(4), 91-96. https://doi.org/10.18100/ijamec.989263
AMA Tokmak M, Şen İ. The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification. International Journal of Applied Mathematics Electronics and Computers. Aralık 2021;9(4):91-96. doi:10.18100/ijamec.989263
Chicago Tokmak, Mahmut, ve İsmail Şen. “The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images With Convolutional Neural Network Classification”. International Journal of Applied Mathematics Electronics and Computers 9, sy. 4 (Aralık 2021): 91-96. https://doi.org/10.18100/ijamec.989263.
EndNote Tokmak M, Şen İ (01 Aralık 2021) The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification. International Journal of Applied Mathematics Electronics and Computers 9 4 91–96.
IEEE M. Tokmak ve İ. Şen, “The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification”, International Journal of Applied Mathematics Electronics and Computers, c. 9, sy. 4, ss. 91–96, 2021, doi: 10.18100/ijamec.989263.
ISNAD Tokmak, Mahmut - Şen, İsmail. “The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images With Convolutional Neural Network Classification”. International Journal of Applied Mathematics Electronics and Computers 9/4 (Aralık 2021), 91-96. https://doi.org/10.18100/ijamec.989263.
JAMA Tokmak M, Şen İ. The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification. International Journal of Applied Mathematics Electronics and Computers. 2021;9:91–96.
MLA Tokmak, Mahmut ve İsmail Şen. “The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images With Convolutional Neural Network Classification”. International Journal of Applied Mathematics Electronics and Computers, c. 9, sy. 4, 2021, ss. 91-96, doi:10.18100/ijamec.989263.
Vancouver Tokmak M, Şen İ. The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification. International Journal of Applied Mathematics Electronics and Computers. 2021;9(4):91-6.

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