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Biologically Rationalized Computing Techniques For Image Processing Applications pp 215–248Cite as

Chlorella Algae Image Analysis Using Artificial Neural Network and Deep Learning

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

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Author information

Authors and Affiliations

  1. Jeppiaar SRR Engineering College, Chennai, Tamil Nadu, India

    S. Lakshmi

  2. Tejas Biotech P Ltd, Chennai, Tamil Nadu, India

    R. Sivakumar

Authors
  1. S. Lakshmi
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  2. R. Sivakumar
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Correspondence to S. Lakshmi .

Editor information

Editors and Affiliations

  1. Karunya University, Coimbatore, India

    Jude Hemanth

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Prof. Dr. Valentina Emilia Balas

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Lakshmi, S., Sivakumar, R. (2018). Chlorella Algae Image Analysis Using Artificial Neural Network and Deep Learning. In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-61316-1_10

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