Identification of Baikal phytoplankton inferred from computer vision methods and machine learning
- Authors: Lysenko А.V.1,2, Oznobikhin М.S.1,2, Kireev Е.А.1,2, Dubrova K.S.1, Vorobyeva S.S.1
-
Affiliations:
- Limnological Institute, Siberian Branch of the Russian Academy of Sciences
- Irkutsk State University
- Issue: No 3 (2021)
- Pages: 1143-1146
- Section: Articles
- URL: https://journals.rcsi.science/2658-3518/article/view/285014
- ID: 285014
Cite item
Abstract
This study discusses the problem of phytoplankton classification using computer vision methods and convolutional neural networks. We created a system for automatic object recognition consisting of two parts: analysis and primary processing of phytoplankton images and development of the neural network based on the obtained information about the images. We developed software that can detect particular objects in images from a light microscope. We trained a convolutional neural network in transfer learning and determined optimal parameters of this neural network and the optimal size of using dataset. To increase accuracy for these groups of classes, we created three neural networks with the same structure. The obtained accuracy in the classification of Baikal phytoplankton by these neural networks was up to 80%.
About the authors
А. V. Lysenko
Limnological Institute, Siberian Branch of the Russian Academy of Sciences; Irkutsk State University
Author for correspondence.
Email: allessouth@gmail.com
Institute of Mathematics and Information Technologies
Russian Federation, 664033, Irkutsk, Ulan-Batorskaya str., 3; 664003, Irkutsk, Gagarina str., 20М. S. Oznobikhin
Limnological Institute, Siberian Branch of the Russian Academy of Sciences; Irkutsk State University
Email: allessouth@gmail.com
Institute of Mathematics and Information Technologies
Russian Federation, 664033, Irkutsk, Ulan-Batorskaya str., 3; 664003, Irkutsk, Gagarina str., 20Е. А. Kireev
Limnological Institute, Siberian Branch of the Russian Academy of Sciences; Irkutsk State University
Email: allessouth@gmail.com
Institute of Mathematics and Information Technologies
Russian Federation, 664033, Irkutsk, Ulan-Batorskaya str., 3; 664003, Irkutsk, Gagarina str., 20K. S. Dubrova
Limnological Institute, Siberian Branch of the Russian Academy of Sciences
Email: allessouth@gmail.com
Russian Federation, 664033, Irkutsk, Ulan-Batorskaya str., 3
S. S. Vorobyeva
Limnological Institute, Siberian Branch of the Russian Academy of Sciences
Email: allessouth@gmail.com
Russian Federation, 664033, Irkutsk, Ulan-Batorskaya str., 3
References
- Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6): 679-698. doi: 10.1109/TPAMI.1986.4767851
- Chollet F. 2017. Xception: deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR.2017.195
- Galdran A., Alvarez-Gila A., Meyer M.I. et al. 2017. Data-driven color augmentation techniques for deep skin image analysis. arXiv:1703.03702v1.
- Hussain M., Bird J.J., Faria D.R. 2018. A study on CNN transfer learning for image classification. In: 18th Annual UK Workshop on Computational Intelligence. doi: 10.1007/978-3-319-97982-3_16
- Larsen-Freeman D. 2013. Transfer of learning transformed. Language Learning 63(s1). doi: 10.1111/j.1467-9922.2012.00740.x
- Liao P.-S., Chen T.-S., Chung P.-C. 2001. A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering 17(5): 713-727. doi: 10.6688/JISE.2001.17.5.1
- Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62-66. doi: 10.1109/TSMC.1979.4310076
- Pratt L., Jennings B. 1996. A survey of transfer between connectionist networks. Connection Science 8(2): 163-184. doi: 10.1080/095400996116866
- Sánchez C., Cristóbal G., Bueno G. 2019. Diatom identification including life cycle stages through morphological and texture descriptors. PeerJ 7. doi: 10.7717/peerj.6770
- Shorten C., Khoshgoftaar T.M. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6(1). doi: 10.1186/s40537-019-0197-0
- Tapas A. 2016. Transfer learning for image classification and plant phenotyping. In: Second International Conference on Electronics, Communication and Aerospace Technology. doi: 10.1109/ICECA.2018.8474802
- Vorobyeva S.S. 2018. Phytoplankton assemblages of the Southern Baikal in 1990-1995 and 2016-2018. Limnology and Freshwater Biology. 1(2): 141-143. doi: 10.31951/2658-3518-2018-A-2-141
- Bondarenko N.A., Vorobyova S.S., Zhuchenko N.A. et al. 2020. Current state of phytoplankton in the littoral area of Lake Baikal, spring 2017. Journal of Great Lakes Research 46(1): 17-28. doi: 10.1016/j.jglr.2019.10.001
- Yang A., Zhang W., Wang J. et al. 2020. Review on the application of machine learning algorithms in the sequence data mining of DNA. Frontiers in Bioengineering and Biotechnology 8. doi: 10.3389/fbioe.2020.01032
- Yamashita R., Nishio M., Do R.K.G. et al. 2018. Convolutional neural networks: an overview and application in radiology. Insights into Imaging 9(4). doi: 10.1007/s13244-018-0639-9
- Majaj N.J., Pelli D.G. 2018. Deep learning - using machine learning to study biological vision. Journal of Vision 18(13):2: 1-13. doi: 10.1167/18.13.2
Supplementary files
