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Hunger classification of Lates calcarifer by means of an automated feeder and image processing

Mohd Razman, Mohd Azraai and Susto, Gian Antonio and Cenedese, Angelo and Abdul Majeed, Anwar P.P. and Musa, Rabiu Muazu and Abdul Ghani, Ahmad Shahrizan and Adnan, Faiez Azizi and Ismail, Khairul Muttaqin and Taha, Zahari and Mukai, Yukinori (2019) Hunger classification of Lates calcarifer by means of an automated feeder and image processing. Computers and Electronics in Agriculture, 163. pp. 1-8. ISSN 01681699

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In an automated demand feeder system, underlining the parameters that contribute to fish hunger is crucial in order to facilitate an optimised food allocation to the fish. The present investigation is carried out to classify the hunger state of Lates calcarifer. A video surveillance technique is employed for data collection. The video was taken throughout the daytime, and the fish were fed through an automated feeding system. It was demonstrated through this investigation that the use of such automated system does contribute towards a higher specific growth rate percentage of body weight as well as the total length by approximately 26.00% and 15.00%, respectively against the conventional time-based method. Sixteen features were feature engineered from the raw dataset into window sizes ranging from 0.5 min, 1.0 min, 1.5 min and 2.0 min, respectively coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes. In addition, the extracted features were analysed through Principal Component Analysis (PCA) for dimensionality reduction as well as PCA with varimax rotation. The data were then classified using a Support Vector Machine (SVM), k- Nearest Neighbor (k-NN) and Random Forest Tree models. It was demonstrated that the varimax based PCA yielded the highest classification accuracy with eight identified features. The prediction results based of the developed k-NN model on the selected features on the test data exhibited a classification rate of 96.5% was achieved suggesting that the features examined are non-trivial in classifying the fish hunger behaviour.

Item Type: Article (Journal)
Additional Information: 6616/75971
Uncontrolled Keywords: Fish feeding behaviour, Video processing, Machine learning Features selection, PCA, varimax rotation,
Subjects: S Agriculture > SH Aquaculture. Fisheries. Angling
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Science
Kulliyyah of Science > Department of Marine Science
Depositing User: Dr. Yukinori Mukai
Date Deposited: 27 Nov 2019 16:59
Last Modified: 27 Nov 2019 16:59
URI: http://irep.iium.edu.my/id/eprint/75971

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