Öz
Teknolojik gelişmeler; akıllı mobil cihazlar, dijital kameralar gibi gereçlerin artmasına ve yaygın olarak kullanımına vesile olmuştur. Bu gelişmeler Derin Öğrenme gibi modern makine öğrenimi yöntemleriyle birlikte biyolojik görüntü verilerindeki artışı da beraberinde getirmiştir. Bu hızlı artış, otomatikleştirilmiş tür tanımlaması için araştırmacılara fırsatlar sunmaktadır. Bu çalışmada; Ichneumonidae alt familyalarının belirlenmesi için Derin Öğrenme yöntemlerinden biri olan konvolüsyonel sinir ağlarına odaklanılmıştır. Bu işlem için ResNet-152 konvolüsyonel sinir ağı mimarisi kullanılmıştır. Deneysel çalışmalar sonucunda %91.35 oranında doğruluk elde edilmiştir.
Anahtar Kelimeler
Kaynakça
- Barbedo, J. G. A. (2020). Detecting and classifying pests in crops using proximal images and machine learning: A review. AI, 1(2), 312–328.
- Chen, G., Han, T. X., He, Z., Kays, R., Forrester, T. (2014). Deep convolutional neural network based species recognition for wild animal monitoring. 2014 IEEE International Conference on Image Processing (ICIP), 858–862. IEEE.
- Dyrmann, M., Karstoft, H., Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72–80. doi: 10.1016/j.biosystemseng.2016.08.024
- Gülcü, A., Kuş, Z. (2019). Konvolüsyonel Sinir Ağlarında Hiper-Parametre Optimizasyonu Yöntemlerinin İncelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 503–522. doi: 10.29109/gujsc.514483
- Hussain, M., Bird, J. J., Faria, D. R. (2018). A study on cnn transfer learning for image classification. UK Workshop on Computational Intelligence, 191–202. Springer.
- INaturalist. (2022). Retrieved 8 February 2022, from INaturalist website: https://www.inaturalist.org/
- Marques, A. C. R., M. Raimundo, M., B. Cavalheiro, E. M., FP Salles, L., Lyra, C., J. Von Zuben, F. (2018). Ant genera identification using an ensemble of convolutional neural networks. Plos One, 13(1), e0192011.
- O’Shea, K., Nash, R. (2015). An introduction to convolutional neural networks. ArXiv Preprint ArXiv:1511.08458.
- Rajeena PP, F., Orban, R., Vadivel, K. S., Subramanian, M., Muthusamy, S., Elminaam, D. S. A., … Ali, M. A. (2022). A novel method for the classification of butterfly species using pre-trained CNN models. Electronics, 11(13), 2016.
- Theivaprakasham, H. (2021). Identification of Indian butterflies using deep convolutional neural network. Journal of Asia-Pacific Entomology, 24(1), 329–340.
- Tokmak, M., Kıraç, A. (2021). Evrişimsel Sinir Ağları ile Örümcek Kuşugillerin Bazı Türlerinin Sınıflandırılması. Bilge International Journal of Science and Technology Research, 5(1), 72–79.
- 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.
- Wäldchen, J., Mäder, P. (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), 2216–2225.
- Zhang, L., Li, H., Zhu, R., Du, P. (2022). An infrared and visible image fusion algorithm based on ResNet-152. Multimedia Tools and Applications, 81(7), 9277–9287. doi: 10.1007/s11042-021-11549-w
Öz
Technological developments; It has led to the increase and widespread use of devices such as smart mobile devices and digital cameras. These developments have brought about the increase in biological image data along with modern machine learning methods such as Deep Learning. This rapid increase offers researchers opportunities for automated species identification. In this study; Convolutional neural networks, one of the Deep Learning methods, have been focused on to determine Ichneumonidae subfamilies. ResNet-152 convolutional neural network architecture is used for this process. As a result of experimental studies, an accuracy of 91.35% was obtained.
Anahtar Kelimeler
Convolutional Neural Networks resnet Ichneumonidae Subfamily
Kaynakça
- Barbedo, J. G. A. (2020). Detecting and classifying pests in crops using proximal images and machine learning: A review. AI, 1(2), 312–328.
- Chen, G., Han, T. X., He, Z., Kays, R., Forrester, T. (2014). Deep convolutional neural network based species recognition for wild animal monitoring. 2014 IEEE International Conference on Image Processing (ICIP), 858–862. IEEE.
- Dyrmann, M., Karstoft, H., Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72–80. doi: 10.1016/j.biosystemseng.2016.08.024
- Gülcü, A., Kuş, Z. (2019). Konvolüsyonel Sinir Ağlarında Hiper-Parametre Optimizasyonu Yöntemlerinin İncelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 503–522. doi: 10.29109/gujsc.514483
- Hussain, M., Bird, J. J., Faria, D. R. (2018). A study on cnn transfer learning for image classification. UK Workshop on Computational Intelligence, 191–202. Springer.
- INaturalist. (2022). Retrieved 8 February 2022, from INaturalist website: https://www.inaturalist.org/
- Marques, A. C. R., M. Raimundo, M., B. Cavalheiro, E. M., FP Salles, L., Lyra, C., J. Von Zuben, F. (2018). Ant genera identification using an ensemble of convolutional neural networks. Plos One, 13(1), e0192011.
- O’Shea, K., Nash, R. (2015). An introduction to convolutional neural networks. ArXiv Preprint ArXiv:1511.08458.
- Rajeena PP, F., Orban, R., Vadivel, K. S., Subramanian, M., Muthusamy, S., Elminaam, D. S. A., … Ali, M. A. (2022). A novel method for the classification of butterfly species using pre-trained CNN models. Electronics, 11(13), 2016.
- Theivaprakasham, H. (2021). Identification of Indian butterflies using deep convolutional neural network. Journal of Asia-Pacific Entomology, 24(1), 329–340.
- Tokmak, M., Kıraç, A. (2021). Evrişimsel Sinir Ağları ile Örümcek Kuşugillerin Bazı Türlerinin Sınıflandırılması. Bilge International Journal of Science and Technology Research, 5(1), 72–79.
- 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.
- Wäldchen, J., Mäder, P. (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), 2216–2225.
- Zhang, L., Li, H., Zhu, R., Du, P. (2022). An infrared and visible image fusion algorithm based on ResNet-152. Multimedia Tools and Applications, 81(7), 9277–9287. doi: 10.1007/s11042-021-11549-w
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 9 Sayı: 18 |
