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

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 emp...

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Main Authors:	Mohd Azraai, Mohd Razman, Susto, Gian Antonio, Cenedese, Angelo, Anwar, P. P. Abdul Majeed, Musa, Rabiu Muazu, Ahmad Shahrizan, Abdul Ghani, Faeiz Azizi, Adnan, Khairul Muttaqin, Ismail, Zahari, Taha, Mukai, Yukinori
Format:	Article
Language:	English
Published:	Elsevier B.V. 2019
Subjects:	SH Aquaculture. Fisheries. Angling
Online Access:	http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/1/Hunger%20classification%20of.pdf

id	ump-25378
recordtype	eprints
spelling	ump-253782019-07-16T02:12:21Z http://umpir.ump.edu.my/id/eprint/25378/ Hunger classification of Lates calcarifer by means of an automated feeder and image processing Mohd Azraai, Mohd Razman Susto, Gian Antonio Cenedese, Angelo Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Ahmad Shahrizan, Abdul Ghani Faeiz Azizi, Adnan Khairul Muttaqin, Ismail Zahari, Taha Mukai, Yukinori SH Aquaculture. Fisheries. Angling 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 0.5â�¯min, 1.0â�¯min, 1.5â�¯min and 2.0â�¯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. Elsevier B.V. 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25378/1/Hunger%20classification%20of.pdf Mohd Azraai, Mohd Razman and Susto, Gian Antonio and Cenedese, Angelo and Anwar, P. P. Abdul Majeed and Musa, Rabiu Muazu and Ahmad Shahrizan, Abdul Ghani and Faeiz Azizi, Adnan and Khairul Muttaqin, Ismail and Zahari, Taha 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 0168-1699 https://doi.org/10.1016/j.compag.2019.104883 https://doi.org/10.1016/j.compag.2019.104883
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	SH Aquaculture. Fisheries. Angling
spellingShingle	SH Aquaculture. Fisheries. Angling Mohd Azraai, Mohd Razman Susto, Gian Antonio Cenedese, Angelo Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Ahmad Shahrizan, Abdul Ghani Faeiz Azizi, Adnan Khairul Muttaqin, Ismail Zahari, Taha Mukai, Yukinori Hunger classification of Lates calcarifer by means of an automated feeder and image processing
description	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 0.5â�¯min, 1.0â�¯min, 1.5â�¯min and 2.0â�¯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.
format	Article
author	Mohd Azraai, Mohd Razman Susto, Gian Antonio Cenedese, Angelo Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Ahmad Shahrizan, Abdul Ghani Faeiz Azizi, Adnan Khairul Muttaqin, Ismail Zahari, Taha Mukai, Yukinori
author_facet	Mohd Azraai, Mohd Razman Susto, Gian Antonio Cenedese, Angelo Anwar, P. P. Abdul Majeed Musa, Rabiu Muazu Ahmad Shahrizan, Abdul Ghani Faeiz Azizi, Adnan Khairul Muttaqin, Ismail Zahari, Taha Mukai, Yukinori
author_sort	Mohd Azraai, Mohd Razman
title	Hunger classification of Lates calcarifer by means of an automated feeder and image processing
title_short	Hunger classification of Lates calcarifer by means of an automated feeder and image processing
title_full	Hunger classification of Lates calcarifer by means of an automated feeder and image processing
title_fullStr	Hunger classification of Lates calcarifer by means of an automated feeder and image processing
title_full_unstemmed	Hunger classification of Lates calcarifer by means of an automated feeder and image processing
title_sort	hunger classification of lates calcarifer by means of an automated feeder and image processing
publisher	Elsevier B.V.
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/ http://umpir.ump.edu.my/id/eprint/25378/1/Hunger%20classification%20of.pdf
first_indexed	2023-09-18T22:38:56Z
last_indexed	2023-09-18T22:38:56Z
_version_	1777416783981969408

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

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