The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by f...

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Main Authors: Zahari, Taha, M. A. M., Razman, F. A., Adnan, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, R. M., Musa, M. F., Sallehudin, Mukai, Y.
Format: Conference or Workshop Item
Language:English
Published: IOP Publishing 2018
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/17627/
http://umpir.ump.edu.my/id/eprint/17627/
http://umpir.ump.edu.my/id/eprint/17627/13/The%20Identification%20of%20Hunger%20Behaviour%20of-fkp-2018.pdf
id ump-17627
recordtype eprints
spelling ump-176272018-07-16T06:42:25Z http://umpir.ump.edu.my/id/eprint/17627/ The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine Zahari, Taha M. A. M., Razman F. A., Adnan Ahmad Shahrizan, Abdul Ghani Anwar, P. P. Abdul Majeed R. M., Musa M. F., Sallehudin Mukai, Y. TS Manufactures Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes. IOP Publishing 2018 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/17627/13/The%20Identification%20of%20Hunger%20Behaviour%20of-fkp-2018.pdf Zahari, Taha and M. A. M., Razman and F. A., Adnan and Ahmad Shahrizan, Abdul Ghani and Anwar, P. P. Abdul Majeed and R. M., Musa and M. F., Sallehudin and Mukai, Y. (2018) The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine. In: IOP Conference Series: Materials Science and Engineering, The 4th Asia Pacific Conference on Manufacturing Systems and the 3rd International Manufacturing Engineering Conference, 7-8 December 2017 , Yogyakarta, Indonesia. pp. 1-5., 319 (012028). ISSN 1757-899X https://doi.org/10.1088/1757-899X/319/1/012028
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Zahari, Taha
M. A. M., Razman
F. A., Adnan
Ahmad Shahrizan, Abdul Ghani
Anwar, P. P. Abdul Majeed
R. M., Musa
M. F., Sallehudin
Mukai, Y.
The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
description Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes.
format Conference or Workshop Item
author Zahari, Taha
M. A. M., Razman
F. A., Adnan
Ahmad Shahrizan, Abdul Ghani
Anwar, P. P. Abdul Majeed
R. M., Musa
M. F., Sallehudin
Mukai, Y.
author_facet Zahari, Taha
M. A. M., Razman
F. A., Adnan
Ahmad Shahrizan, Abdul Ghani
Anwar, P. P. Abdul Majeed
R. M., Musa
M. F., Sallehudin
Mukai, Y.
author_sort Zahari, Taha
title The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
title_short The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
title_full The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
title_fullStr The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
title_full_unstemmed The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine
title_sort identification of hunger behaviour of lates calcarifer through the integration of image processing technique and support vector machine
publisher IOP Publishing
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/17627/
http://umpir.ump.edu.my/id/eprint/17627/
http://umpir.ump.edu.my/id/eprint/17627/13/The%20Identification%20of%20Hunger%20Behaviour%20of-fkp-2018.pdf
first_indexed 2023-09-18T22:24:28Z
last_indexed 2023-09-18T22:24:28Z
_version_ 1777415873460436992

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