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Chlorella Algae Image Analysis Using Artificial Neural Network and Deep Learning

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Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 25)

Abstract

Generally, solutions and results to a problem in image processing involve a lot of trail and testing with huge set of sample images. Chlorella is a single-cell, freshwater green algae, and it consists of green plant pigments, chlorophyll, vitamins, minerals, and protein, fiber, and omega fatty acids. The size of the chlorella cells are 10–30 μm. Due to its photosynthetic process, it converts carbon dioxide into fresh oxygen. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. In this chapter, an elaborate analysis of artificial neural network concepts and convolutional neural network (CNN) of deep learning technique that automatically measure the algae growth through the image classification techniques from algae digital images using MATLAB is presented.

Keywords

Digital image processing Convolution neural networks (CNN) MATLAB Back propagation Radial basis Probability neural network Chlorella Algae 

References

  1. 1.
    Castelluccio M, Poggi G, Sansone C, Verdoliva L (2016) Land use classification in remote sensing images by convolutional neural networks. Available online: http://arxiv.org/abs/1508.00092. Accessed on 12 Apr 2016
  2. 2.
    Couprie C, Farabet C, LeCun Y, Najman L (2013) Indoor semantic segmentation using depth information. In: Proceedings of the international conference on learning representation. Scottsdale, Arizona, 2–4 May 2013Google Scholar
  3. 3.
    Elaksher AF (2008) Multi-image matching using neural networks and photogrammetric conditions. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, vol XXXVII. Part I, B3a. Beijing 2008Google Scholar
  4. 4.
    Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35:1915–1929CrossRefGoogle Scholar
  5. 5.
    French M, Recknagel F, Jarrett GL (1998) Scaling issues in artificial neural network modelling and forecasting of algal bloom dynamics. In: Abt SR, Young Pezeshk J, Watson CC (eds) Proceedings of the international water resources engineering conference, ASCE, vol 1. Memphis, Tennessee, 3–7 Aug 1998, pp 891–896Google Scholar
  6. 6.
    Gonzalez RC, Woods RE (2008) Digital image processing. Gatesmark PublishingGoogle Scholar
  7. 7.
    Gonzalez RC, Woods RE, Eddins S (2009) Digital image processing using matlab. Gatesmark PublishingGoogle Scholar
  8. 8.
    Hu F, Xia GS, Hu J, Zhang L (2015) Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 7:14680–14707Google Scholar
  9. 9.
    Jähne B (2002) Digital image processing. 5th rev. Springer, London Google Scholar
  10. 10.
    Joseph HWL, Huang Y, Dickman M, Jayawardena AW (2003) Neural network modeling of coastal algal blooms. In: Ecological Modelling, vol 159. Elsevier Science B.V., pp 179–201 Google Scholar
  11. 11.
    Junna C, Ji G, Feng C, Zheng H (2009) Application of connected morphological operators to image smoothing and edge detection of algae. In: International conference on information technology and computer science, pp 73–76Google Scholar
  12. 12.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, p 4Google Scholar
  13. 13.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems; Curran Associates, North Miami Beach, FL, USA, pp 1097–1105Google Scholar
  14. 14.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. IEEE Proc. 86:2278–2324Google Scholar
  15. 15.
    Lee HS, Lee JHW (1995) Continuous monitoring of short term dissolved oxygen and algal dynamics. Water Res 29(12):2789–2796Google Scholar
  16. 16.
    Lek S, Guegan JF (1999) Artificial neural networks as a tool in ecological modeling, an introduction. Ecol. Model. 120:65–73Google Scholar
  17. 17.
    Merchant RE, Andre CA (2001) A review of recent clinical trials of the nutritional supplement Chlorella pyrenoidosa in the treatment of fibromyalgia, hypertension, and ulcerative colitis. Altern Ther Health Med 7(3):79–91. Review. PubMed PMID: 11347287Google Scholar
  18. 18.
    Mohammed TS, Al-Taie NI (2012) Artificial neural networks as decision-makers for stereo matching. GSTF Int J Comput 1(3)Google Scholar
  19. 19.
    Parsons TR, Takahashi M, Hargrave B (1984) Biological oceanographic processes. Pergamon, OxfordGoogle Scholar
  20. 20.
    Penatti OA, Nogueira K, dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Boston, MA, USA, 7–12 June 2015Google Scholar
  21. 21.
    Phung SL, Bouzerdoum A. MATLAB library for convolutional neural network. Technical report. ICT Research Institute, Visual and Audio Signal Processing Laboratory, University of Wollongong. Available at: http://www.uow.edu.au/˜phung
  22. 22.
    Quigley M, Batra S, Gould S, Klingbeil E, Le QV, Wellman A, Ng AY (2009) High-accuracy 3D sensing for mobile manipulation: improving object detection and door opening. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), Kobe, Japan, 12–17 May 2009, pp 2816–2822Google Scholar
  23. 23.
    Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: 2014 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 512–519Google Scholar
  24. 24.
    Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network approach for modeling and prediction of algal blooms. Ecol Model 96:11–28CrossRefGoogle Scholar
  25. 25.
    Sandesh BK, Shalini C, Brinda BR, Kumar MA (2005) Digital image processing—an alternate tool for monitoring of pigment levels in cultured cells with special reference to green alga Haematococcus pluvialis. Biosens Bioelectron 21:768–773CrossRefGoogle Scholar
  26. 26.
    Shanmugam K, Haralick RM, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621Google Scholar
  27. 27.
    Socher R, Huval B, Bath B, Manning CD, Ng AY (2012) Convolutional-recursive deep learning for 3D object classification. In: Advances in neural information processing systems. Curran Associates, North Miami Beach, FL, USA, pp 665–673Google Scholar
  28. 28.
    Thomann RV, Mueller JA (1987) Principles of surface water quality modeling and control. Harper and Row, New YorkGoogle Scholar
  29. 29.
    Tutorial on deep learning [Online]. Available at: http://deeplearning.net/tutorial/lenet.html
  30. 30.
    Whitehead PG, Howard A, Arulmani C (1997) Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic mass balance and neural network techniques. Hydrobiologia 349:39–46Google Scholar
  31. 31.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Jeppiaar SRR Engineering CollegeChennaiIndia
  2. 2.Tejas Biotech P LtdChennaiIndia

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