Xiangyu Ye
ABSTRACT
In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria.
- Sang-Soo Baek, JongCheol Pyo, Yakov Pachepsky, Yongeun Park, Mayzonee Ligaray, Chi-Yong Ahn, Young-Hyo Kim, Jong Ahn Chun, and Kyung Hwa Cho. 2020. Identification and enumeration of cyanobacteria species using a deep neural network. Ecological Indicators 115 (2020), 106395. https://doi.org/10.1016/j.ecolind.2020.106395Google Scholar
Cross Ref
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4(2017), 834–848.Google Scholar
- Qiuwen Chen and Arthur E Mynett. 2004. Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression. Ecological Modelling 176, 3 (2004), 277–290. https://doi.org/10.1016/j.ecolmodel.2003.10.031Google ScholarCross Ref
- Matt W Gardner and SR Dorling. 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment 32, 14-15 (1998), 2627–2636.Google Scholar
- Jef Huisman, Geoffrey A Codd, Hans W Paerl, Bas W Ibelings, Jolanda MH Verspagen, and Petra M Visser. 2018. Cyanobacterial blooms. Nature Reviews Microbiology 16, 8 (2018), 471–483.Google ScholarCross Ref
- Yordanka Karayaneva and Diana Hintea. 2018. Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers. Journal of Image and Graphics 6, 1 (2018).Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 6 (2017), 84–90.Google ScholarDigital Library
- Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324. https://doi.org/10.1109/5.726791Google ScholarCross Ref
- Qichun Liang, Yuchao Zhang, Ronghua Ma, Steven Loiselle, Jing Li, and Minqi Hu. 2017. A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu. Remote Sensing 9, 2 (2017). https://doi.org/10.3390/rs9020133Google Scholar
- Shudong Liu and Yunfei Li. 2010. Real-time simulation of blue-green algae outburst in Taihu Lake. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Vol. 1. IEEE, V1–637.Google Scholar
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440.Google ScholarCross Ref
- Mark William Matthews, Stewart Bernard, and Lisl Robertson. 2012. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment 124 (2012), 637–652. https://doi.org/10.1016/j.rse.2012.05.032Google ScholarCross Ref
- Hee-Mock Oh, Chi-Yong Ahn, Jae-Won Lee, Tae-Soo Chon, Kyung Hee Choi, and Young-Seuk Park. 2007. Community patterning and identification of predominant factors in algal bloom in Daechung Reservoir (Korea) using artificial neural networks. Ecological Modelling 203, 1 (2007), 109–118. https://doi.org/10.1016/j.ecolmodel.2006.04.030 Special Issue on Ecological Informatics: Biologically-Inspired Machine Learning.Google ScholarCross Ref
- Meie Pan, Xudong Zhao, Quanli Xu, Shuangyun Peng, Liang Hong, 2012. Remote sensing recognition, concentration classification and dynamic analysis of cyanobacteria bloom in Dianchi Lake based on MODIS data. In 2012 20th International Conference on Geoinformatics. IEEE, 1–5.Google Scholar
- JongCheol Pyo, Hongtao Duan, Sangsoo Baek, Moon Sung Kim, Taegyun Jeon, Yong Sung Kwon, Hyuk Lee, and Kyung Hwa Cho. 2019. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment 233 (2019), 111350. https://doi.org/10.1016/j.rse.2019.111350Google ScholarCross Ref
- JongCheol Pyo, Lan Joo Park, Yakov Pachepsky, Sang-Soo Baek, Kyunghyun Kim, and Kyung Hwa Cho. 2020. Using convolutional neural network for predicting cyanobacteria concentrations in river water. Water Research 186(2020), 116349. https://doi.org/10.1016/j.watres.2020.116349Google ScholarCross Ref
- U. F. Rahim and H. Mineno. 2020. Tomato Flower Detection and Counting in Greenhouses Using Faster Region-Based Convolutional Neural Network. Journal of Image and Graphics 8, 4 (2020), 107–113.Google ScholarCross Ref
- Thomas Serre, Gabriel Kreiman, Minjoon Kouh, Charles Cadieu, Ulf Knoblich, and Tomaso Poggio. 2007. A quantitative theory of immediate visual recognition. In Computational Neuroscience: Theoretical Insights into Brain Function, Paul Cisek, Trevor Drew, and John F. Kalaska (Eds.). Progress in Brain Research, Vol. 165. Elsevier, 33–56. https://doi.org/10.1016/S0079-6123(06)65004-8Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014).Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going Deeper With Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
- Oliver Urbann and Jonas Stenzel. 2019. A convolutional neural network that self-contained counts. Journal of Image and Graphics 7, 4 (2019), 112–116.Google ScholarCross Ref
- Robert K Vincent, Xiaoming Qin, R.Michael L McKay, Jeffrey Miner, Kevin Czajkowski, Jeffrey Savino, and Thomas Bridgeman. 2004. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment 89, 3 (2004), 381–392. https://doi.org/10.1016/j.rse.2003.10.014Google ScholarCross Ref
- Yen-Ju Wu, Chun-Ming Tsai, and Frank Shih. 2016. Improving leaf classification rate via background removal and ROI extraction. Journal of Image and Graphics 4, 2 (2016), 93–98.Google ScholarCross Ref
- Kai Yu, Yuanqing Lin, and John Lafferty. 2011. Learning image representations from the pixel level via hierarchical sparse coding. In CVPR 2011. 1713–1720. https://doi.org/10.1109/CVPR.2011.5995732Google ScholarDigital Library
- Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, and Rob Fergus. 2010. Deconvolutional networks. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2528–2535. https://doi.org/10.1109/CVPR.2010.5539957Google ScholarCross Ref
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- Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network
Index terms have been assigned to the content through auto-classification.
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