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
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Abstract
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.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Fish Feeding Behaviour; Support Vector Machine; Lates Clacarifer |
| Subjects: | T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Manufacturing Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 16 Jul 2018 06:42 |
| Last Modified: | 16 Jul 2018 06:42 |
| URI: | http://umpir.ump.edu.my/id/eprint/17627 |
| Download Statistic: | View Download Statistics |
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