An artificial neural network (ANN) model for the cell density measurement of spirulina (A. platensis)
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018
Publication Date
3-12-2019
Abstract
This paper presents a dynamic model for the cell density measurement of Spirulina platensis by using backpropagation-based Artificial Neural Network (ANN). A vision system, composed of a camera and a photodetector, is developed to measure the color features and illuminance of the algal culture, which will then serve as the training data. The input parameters are the RGB values and the lux value from the vision system. The network has three layers with structure 4 - X - 1, where the node size X of the hidden layer is varied experimentally. After several trials of training, the model with 24 nodes showed the lowest mean squared error of 0.0047813 and fastest learning time of 2 seconds. This model was validated by performing F-test on the actual dataset and the output from the model. Results show that there is no significant statistical difference between the two, and that the output from the ANN is valid. © 2018 IEEE.
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Digitial Object Identifier (DOI)
10.1109/HNICEM.2018.8666297
Recommended Citation
Aquino, A. U., Fernandez, M. M., Guzman, A. P., Matias, A. A., Valenzuela, I. C., & Dadios, E. P. (2019). An artificial neural network (ANN) model for the cell density measurement of spirulina (A. platensis). 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 https://doi.org/10.1109/HNICEM.2018.8666297
Disciplines
Manufacturing
Keywords
Freshwater algae; Cytometry; Neural networks (Computer science)
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