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 b...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Physics Publishing
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/65312/ http://irep.iium.edu.my/65312/ http://irep.iium.edu.my/65312/ http://irep.iium.edu.my/65312/1/65312_The%20Identification%20of%20Hunger%20Behaviour%20of%20Lates_conference%20article.pdf http://irep.iium.edu.my/65312/2/65312_The%20Identification%20of%20Hunger%20Behaviour%20of%20Lates_scopus.pdf |
| Summary: | 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. |
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