Endemic Plant Classification Using Deep Neural Networks

Main Article Content

Melih Öz
Alper Özcan


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

Article Details

How to Cite
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


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: https://doi.org/10.1016/j.biocon.2011.06.025

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: https://doi.org/10.1016/j.jnc.2010.01.002

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: https://doi.org/10.3390/plants9030345

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: https://doi.org/10.1017/S0030605313000288

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: https://doi.org/10.3906/bot-1012-90

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

F. Chollet, Deep learning with Python. Manning Publications Company, 2017.

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. DOI: https://doi.org/10.1109/CVPR.2009.5206848

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/

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.pdf

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.pdf

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.

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: https://doi.org/10.3390/ani10071207

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: https://doi.org/10.21923/jesd.828457


“Türkiyebitkileri.com - Anasayfa,” Oct. 22, 2022. https://turkiyebitkileri.com/tr/ (accessed Mar. 07, 2023).

“agaclar.net.” http://www.agaclar.net/ (accessed Mar. 08, 2023).

“Türkiye Bitkileri Listesi // bizimbitkiler.org.tr - Nezahat Gökyiğit Botanik Bahçesi - 2013.” https://www.bizimbitkiler.org.tr/v2/index.php (accessed Mar. 07, 2023).

G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.

F. Chollet and others, Keras. GitHub, 2015. [Online]. Available: https://github.com/fchollet/keras

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: https://doi.org/10.1186/s12880-015-0068-x

Martín Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.” 2015. [Online]. Available: https://www.tensorflow.org/