O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Endemic Plant Classification Using Deep Neural Networks

Melih Öz1,
Alper Özcan2
1Akdeniz University
2Akdeniz University
Published:March 28, 2023
DOI: 10.56038/oprd.v2i1.252
Vol. 2, No. 1 · pp. 59–67

Abstract

Endemic plants are those that are native to a specific geographic region and are found nowhere else in the world. These plants are crucial for biodiversity, conservation, cultural significance, and economic value. Turkey hosts more than 4000 endemic plants. Therefore, this makes Turkey the richest in Europe. Preserving this habitat holds importance. This study aims to conceptualize a possible application that helps individuals to identify endemic species using camera-captured images. Thus, aiding the preservation of the habitat. In this study, 23 selected species of Turkey’s endemic biodiversity are classified using Deep Neural Network built. In line with the objective of this study, a dataset containing 253 images is created to train the network. The dataset is available at: github.com/melihoz/endemicdataset

Keywords
endemicplantclassificationdeep neural networks

References

  1. 1.Ç. H. Şekercioğlu et al., “Turkey’s globally important biodiversity in crisis,” Biological Conservation, vol. 144, no. 12, pp. 2752–2769, Dec. 2011, doi: 10.1016/j.biocon.2011.06.025.DOI
  2. 2.C. Türe and H. Böcük, “Distribution patterns of threatened endemic plants in Turkey: A quantitative approach for conservation,” Journal for Nature Conservation, vol. 18, no. 4, pp. 296–303, Dec. 2010, doi: 10.1016/j.jnc.2010.01.002.DOI
  3. 3.N. Coelho, S. Gonçalves, and A. Romano, “Endemic Plant Species Conservation: Biotechnological Approaches,” Plants, vol. 9, no. 3, Art. no. 3, Mar. 2020, doi: 10.3390/plants9030345.DOI
  4. 4.B. Foggi, D. Viciani, R. M. Baldini, A. Carta, and T. Guidi, “Conservation assessment of the endemic plants of the Tuscan Archipelago, Italy,” Oryx, vol. 49, no. 1, pp. 118–126, Jan. 2015, doi: 10.1017/S0030605313000288.DOI
  5. 5.K. IŞIK, “Rare and endemic species: why are they prone to extinction?,” Turkish Journal of Botany, vol. 35, no. 4, pp. 411–417, Jan. 2011, doi: 10.3906/bot-1012-90.DOI
  6. 6.I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
  7. 7.F. Chollet, Deep learning with Python. Manning Publications Company, 2017.
  8. 8.J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition, 2009, pp. 248–255.
  9. 9.A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” 2012. [Online]. Available: http://code.google.com/p/cuda-convnet/Link
  10. 10.I. Goodfellow et al., “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2672–2680. Accessed: May 21, 2019. [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdfLink
  11. 11.C. Szegedy, A. Toshev, and D. Erhan, “Deep Neural Networks for Object Detection,” in Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 2553–2561. Accessed: May 15, 2019. [Online]. Available: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdfLink
  12. 12.M. Yang, J. J. Yang, Q. Zhang, Y. Niu, and J. Li, “Classification of retinal image for automatic cataract detection,” in 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013, 2013, pp. 674–679.
  13. 13.H. G. Akçay, B. Kabasakal, D. Aksu, N. Demir, M. Öz, and A. Erdoğan, “Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping,” Animals, vol. 10, no. 7, Art. no. 7, Jul. 2020, doi: 10.3390/ani10071207.DOI
  14. 14.T. Danişman et al., “PREDICTING THE LOCATION OF THE UTERINE CERVICAL OS FROM 2D IMAGES WITH CNN,” Mühendislik Bilimleri ve Tasarım Dergisi, vol. 8, no. 5, Art. no. 5, Dec. 2020, doi: 10.21923/jesd.828457.DOI
  15. 15.Sefa AKBULUT, Zafer Cemal ÖZKAN, ORMAN BOTANİĞİ DERS NOTLARI.
  16. 16.“Türkiyebitkileri.com - Anasayfa,” Oct. 22, 2022. https://turkiyebitkileri.com/tr/ (accessed Mar. 07, 2023).Link
  17. 17.“agaclar.net.” http://www.agaclar.net/ (accessed Mar. 08, 2023).Link
  18. 18.“Türkiye Bitkileri Listesi // bizimbitkiler.org.tr - Nezahat Gökyiğit Botanik Bahçesi - 2013.” https://www.bizimbitkiler.org.tr/v2/index.php (accessed Mar. 07, 2023).Link
  19. 19.G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.
  20. 20.F. Chollet and others, Keras. GitHub, 2015. [Online]. Available: https://github.com/fchollet/kerasLink
  21. 21.A. A. Taha and A. Hanbury, “Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool,” BMC Medical Imaging, vol. 15, no. 1, p. 29, Aug. 2015, doi: 10.1186/s12880-015-0068-x.DOI
  22. 22.Martín Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.” 2015. [Online]. Available: https://www.tensorflow.org/Link
Download PDF
Cite This Article
Öz, M., Özcan, A. (2023). Endemic Plant Classification Using Deep Neural Networks. *Orclever Proceedings of Research and Development*, 2(1), 59-67. https://doi.org/10.56038/oprd.v2i1.252

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume2
Issue1
Pages59–67
PublishedMarch 28, 2023
eISSN2980-020X