Machine Vision Support System for Monitoring Water Quality in a Small Scale Tiger Prawn Aquaculture

Reggie C. Gustilo; Elmer P. Dadios

doi:10.20965/jaciii.2016.p0111

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JACIII Vol.20 No.1 pp. 111-116

doi: 10.20965/jaciii.2016.p0111

(2016)

Paper:

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Machine Vision Support System for Monitoring Water Quality in a Small Scale Tiger Prawn Aquaculture

Reggie C. Gustilo^* and Elmer P. Dadios^**

^*Electronics and Communications Engineering Department, De La Salle University
2401 Taft Ave, Manila 1004, Philippines

^**Manufacturing Engineering and Management Department, De La Salle University
2401 Taft Ave, Manila 1004, Philippines

Received:

June 30, 2015

Accepted:

September 10, 2015

Online released:

January 19, 2016

Published:

January 20, 2016

Keywords:

automated aquaculture, fuzzy logic, machine vision, pattern recognition, water quality alarm and monitoring systems

Abstract

A fuzzy-based machine vision system was designed to support an industrial sensor based small scale tiger prawn aquaculture system. This system is based on the behavioral movements of tiger prawns as they are subjected to different stress levels such as unsafe values of dissolved oxygen, temperature, salinity and ph. Each parameter is carefully adjusted to trigger a change in the normal behavior or movement of tiger prawns. The change in the behavior of the tiger prawn, as seen by the machine vision system, may serve as a level detection or an alarm system that will aid the sensor based system in terms of monitoring the water quality of the aquaculture environment. This machine vision system may be used to trigger the actuators needed to correct the dangerous water quality parameter back to its safe level. This research is done in real time using two basic web cameras to support industry grade sensors in maintaining the safe water quality level for this small scale habitat.

Cite this article as:

R. Gustilo and E. Dadios, “Machine Vision Support System for Monitoring Water Quality in a Small Scale Tiger Prawn Aquaculture,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.1, pp. 111-116, 2016.

Data files:

References

[1] R. C. Gustilo, E. P. Dadios, E. Calilung, and L. Ganlim, “Identifying Water Quality Index for Small Scale Tiger Prawn Aquaculture Setup Using Neuro-Fuzzy Techniques,” 6^th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management 2013 (HNICEM 2013), 2013.
[2] R. Wang, D. Chen, and Z. Fu, “AWQEE-DSS: A Decision Support System for Aquaculture Water Quality Evaluation and Early-warning,” Int. Conf. on Computational Intelligence and Security, 2006.
[3] R. C. Gustilo and E. Dadios, “Optimal control of prawn aquaculture water quality index using artificial neural networks,” 2011 IEEE 5^th Int. Conf. on Cybernetics and Intelligent Systems (CIS), 2011.
[4] S. K. Vaddadi, S. S. Sadistap, and P. Kumar, “Development of embedded wireless network and water quality measurement systems for aquaculture,” 6^th Int. Conf. on Sensing Technology (ICST), 2012.
[5] T. Jantakoson, K. Kijroogrojana, and S. Benjukal, “Effect of high pressure and heat treatments on black tiger shrimp (Penaeus monodon Fabricius) muscle protein,” Int. Aquatic Research, Vol.4, p. 19, 2012.
[6] S. S. Rautaray and A. Agrawal, “Real Time Hand Gesture Recognition System for Dynamic Applications,” Int. J. of UbiComp (IJU), Vol.3, No.1, 2012.
[7] B. Dong and B. Andrews, “Sensor-based Occupancy Behavioral Pattern Recognition for Energy and Comfort Management in Intelligent Buildings,” 11^th Int. IBPSA Conf., 2009.
[8] C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Conditional Models for Contextual Human Motion Recognition,” Int. Conf. on Computer Vision, ICCV 2005, 2005.
[9] S. Verwer, M. deWeerdt and C. Witteveen, “Learning Driving Behavior by Timed Syntactic Pattern Recognition,” Proc. of the 22^nd Int. Joint Conf. on Artificial Intelligence, 2011.
[10] N. Ikizler, “Understanding of Human Motion: Recognition and Retrieval of Human Activities,” A Dissertation submitted to the Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.
[11] W. T. Freeman and M. Roth, “Orientation Histograms for Hand Gesture Recognition,” Mitsubishi Electric Research Laboratories, Inc., 1994.
[12] J. M. Abreua, F. C. Pereirab, and P. Ferraoc, “Using pattern recognition to identify habitual behavior in residential electricity Consumption,” Energy and Buildings, Vol.49, pp. 479-487, 2012.
[13] J. P. Varkey, D. Pompili, and T. A. Walls, “Human motion recognition using a wireless sensor-based wearable system,” Springer-Verlag London Limited, 2011.
[14] L. Wendehals and A. Orso, “Recognizing Behavioral Patterns at Runtime using Finite Automata,” WODA’06, 2006.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. C. Gustilo, E. P. Dadios, E. Calilung, and L. Ganlim, “Identifying Water Quality Index for Small Scale Tiger Prawn Aquaculture Setup Using Neuro-Fuzzy Techniques,” 6^th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management 2013 (HNICEM 2013), 2013.

[2] [2] R. Wang, D. Chen, and Z. Fu, “AWQEE-DSS: A Decision Support System for Aquaculture Water Quality Evaluation and Early-warning,” Int. Conf. on Computational Intelligence and Security, 2006.

[3] [3] R. C. Gustilo and E. Dadios, “Optimal control of prawn aquaculture water quality index using artificial neural networks,” 2011 IEEE 5^th Int. Conf. on Cybernetics and Intelligent Systems (CIS), 2011.

[4] [4] S. K. Vaddadi, S. S. Sadistap, and P. Kumar, “Development of embedded wireless network and water quality measurement systems for aquaculture,” 6^th Int. Conf. on Sensing Technology (ICST), 2012.

[5] [5] T. Jantakoson, K. Kijroogrojana, and S. Benjukal, “Effect of high pressure and heat treatments on black tiger shrimp (Penaeus monodon Fabricius) muscle protein,” Int. Aquatic Research, Vol.4, p. 19, 2012.

[6] [6] S. S. Rautaray and A. Agrawal, “Real Time Hand Gesture Recognition System for Dynamic Applications,” Int. J. of UbiComp (IJU), Vol.3, No.1, 2012.

[7] [7] B. Dong and B. Andrews, “Sensor-based Occupancy Behavioral Pattern Recognition for Energy and Comfort Management in Intelligent Buildings,” 11^th Int. IBPSA Conf., 2009.

[8] [8] C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Conditional Models for Contextual Human Motion Recognition,” Int. Conf. on Computer Vision, ICCV 2005, 2005.

[9] [9] S. Verwer, M. deWeerdt and C. Witteveen, “Learning Driving Behavior by Timed Syntactic Pattern Recognition,” Proc. of the 22^nd Int. Joint Conf. on Artificial Intelligence, 2011.

[10] [10] N. Ikizler, “Understanding of Human Motion: Recognition and Retrieval of Human Activities,” A Dissertation submitted to the Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.

[11] [11] W. T. Freeman and M. Roth, “Orientation Histograms for Hand Gesture Recognition,” Mitsubishi Electric Research Laboratories, Inc., 1994.

[12] [12] J. M. Abreua, F. C. Pereirab, and P. Ferraoc, “Using pattern recognition to identify habitual behavior in residential electricity Consumption,” Energy and Buildings, Vol.49, pp. 479-487, 2012.

[13] [13] J. P. Varkey, D. Pompili, and T. A. Walls, “Human motion recognition using a wireless sensor-based wearable system,” Springer-Verlag London Limited, 2011.

[14] [14] L. Wendehals and A. Orso, “Recognizing Behavioral Patterns at Runtime using Finite Automata,” WODA’06, 2006.

Machine Vision Support System for Monitoring Water Quality in a Small Scale Tiger Prawn Aquaculture

Reggie C. Gustilo* and Elmer P. Dadios**

Reggie C. Gustilo^* and Elmer P. Dadios^**