single-jc.php

JACIII Vol.21 No.2 pp. 211-220
doi: 10.20965/jaciii.2017.p0211
(2017)

Paper:

Behavioural Response Analysis Using Vision Engineering (BRAVENet)

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

*ECE Department, De La Salle University
2401 Taft Avenue, Manila, Philippines
**MEM Department, De La Salle University
2401 Taft Avenue, Manila, Philippines

Received:
July 25, 2016
Accepted:
October 31, 2016
Online released:
March 15, 2017
Published:
March 20, 2017
Keywords:
machine vision, aquaculture, fuzzy logic, intelligent systems
Abstract

A new engineering methodology is proposed to improve the automation process in monitoring the water quality in a small scale aquaculture system. Behavioural Response Analysis using Vision Engineering Network or BRAVENet is proposed, as a support system to a traditional sensor-based system, to monitor critical water quality parameters such as temperature, pH, salinity and dissolved oxygen. BRAVENet is based on the reactions or behavioural responses of tiger prawns to different water conditions. The performance of both the sensor-based system and BRAVENet are analysed and discussed. It is shown that the BRAVENet can identify unsafe levels of water parameters and is a good monitoring and prediction tool for water conditions especially those instances when industry grade sensors fail or become erroneous. Promising results show that BRAVENet can be used as a support system, if not as a replacement, in continuously monitoring the status of the critical water quality parameters of aquaculture systems.

References
  1. [1] B. Chang and X. Zhan,“Aquaculture Monitoring System Based on Fuzzy-PID Algorithm and Intelligent Sensor Networks,” Cross Strait Quad-Regional Radio Science and Wireless Technology Conf. (CSQRWC), 2013.
  2. [2] J. J. Carbajal and L. P. Sanchez, “Classification Based on Fuzzy Inference Systems for Artificial Habitat Quality in Shrimp Farming,” 7th Mexican Int. Conf. on Artificial Intelligence, MICAI ’08, 2008.
  3. [3] L. De et al., “MCU-Based Aquaculture System,” Proc. of the World Congress on Engineering and Computer Science 2011 Vol.II, WCECS 2011, San Francisco, USA, October 19-21, 2011.
  4. [4] G. Wiranto et al., “Design of Online Data Measurement and Automatic Sampling System for Continuous Water Quality Monitoring,” IEEE Int. Conf. on Mechatronics and Automation (ICMA), 2015.
  5. [5] S. N. Hidayah et al., “Development of Fuzzy Fish Pond Water,” Colloquium on Humanities, Science and Engineering (CHUSER), 2011.
  6. [6] J. H. Chen et al., “Automated Monitoring System for the Fish Farm Aquaculture Environment,” IEEE Int. Conf. on Systems, Man and Cybernetics (SMC), 2015.
  7. [7] L. Yoot et al., “Prediction of Water Quality Index (WQI) Based on Artificial Neural Network (ANN),” Student Conf. on Research and Development Proc., Shah Alam, Malaysla, 2002.
  8. [8] Gustilo et al., “Machine Vision Support System for Monitoring Water Quality in a Small Scale Tiger Prawn Aquaculture,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.20, No.1, pp. 111-116, 2015.
  9. [9] G. Wiranto et al., “Integrated Online Water Quality Monitoring,” Int. Conf. on Smart Sensors and Applications (ICSSA), 2015.
  10. [10] H. Li and X. Hua, “Water Environment Monitoring System Based on Zigbee Technology,” 3rd Int. Conf. on Intelligent Systems Design and Engineering Applications (ISDEA), 2013.
  11. [11] D. He and L.X. Zhang, “The Water Quality Monitoring System Based on WSN,” 2012 2nd Int. Conf. on Consumer Electronis, Communications and Networks (CECNet), 2012.
  12. [12] M. Hua et al., “The Design of Intelligent Monitor and Control System of Aquaculture Based on Wireless Sensor Networks,” 3rd IEEE Int. Conf. on Computer Science and Information Technology (ICCSIT), 2010.
  13. [13] N. Tang et al., “Automated Monitoring and Control System for Shrimp Farms Based on Embedded System and Wireless Sensor Network,” IEEE Int. Conf. on Electrical, Computer and Communications Technologies (ICECCT), 2015.
  14. [14] C. Beyan and R. B. Fisher, “A filtering mechanism for normal fish trajectories,” 21st Int. Conf. on Pattern Recognition (ICPR), 2012.
  15. [15] D. J. Lee et al., “An Efficient Shape Analysis Method for Shrimp Quality Evaluation,” 12th Int. Conf. on Control Automation Robotics & Vision (ICARCV), 2012.
  16. [16] S. Iwamoto et al., “REFLICS: Real-time Flow Imaging and Classification System,” 15th Int. Conf. on Pattern Recognition, 2000.
  17. [17] J. H. Lee et al., “A Tank Fish Recognition and Tracking System Using Computer Vision Techniques,” 3rd Int. Conf. on Science and Information Technology (ICCSIT), 2010.
  18. [18] K. N. A. K. Adnan, N. Yusuf, H. N. et al., “Water Quality Classification and Monitoring Using E-nose and E-tongue in Aquaculture Farming,” 2nd Int. Conf. on Electronic Design (ICED), 2014.
  19. [19] R. T. Labuguen et al., “Automated Fish Fry Counting and Schooling Behavior Analysis Using Computer Vision,” 8th Int. Colloqium on Signal Proc. and its Applicatons (CSPA), 2012.
  20. [20] J. R. Mathiassen et al., “A Simple Computer Vision Method for Automatic Detection of Melanin Spots in Atlantic Salmon Fillets,” IMVIP Int. Machine Vision and Image Proc. Conf., 2007.
  21. [21] G. T. Shrivakshan, “An Analysis of SOBEL and GABOR Image Filters for Identifying Fish,” Int. Conf. on Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017