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 e...

Full description

Bibliographic Details
Main Authors:	Mohd Razman, Mohd Azraai, Susto, Gian Antonio, Cenedese, Angelo, Abdul Majeed, Anwar P.P., Musa, Rabiu Muazu, Abdul Ghani, Ahmad Shahrizan, Adnan, Faiez Azizi, Ismail, Khairul Muttaqin, Taha, Zahari, Mukai, Yukinori
Format:	Article
Language:	English English
Published:	Elsevier B.V. A 2019
Subjects:	SH Aquaculture. Fisheries. Angling
Online Access:	http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/1/hunger%20classification%20azraai%202019.pdf http://irep.iium.edu.my/75971/7/Scopus%20-%20hunger%20classification%20of%20lates%20calcarifer.pdf

id	iium-75971
recordtype	eprints
spelling	iium-759712019-11-27T08:59:11Z http://irep.iium.edu.my/75971/ Hunger classification of Lates calcarifer by means of an automated feeder and image processing Mohd Razman, Mohd Azraai Susto, Gian Antonio Cenedese, Angelo Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Abdul Ghani, Ahmad Shahrizan Adnan, Faiez Azizi Ismail, Khairul Muttaqin Taha, Zahari 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 0.5 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. Elsevier B.V. A 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/75971/1/hunger%20classification%20azraai%202019.pdf application/pdf en http://irep.iium.edu.my/75971/7/Scopus%20-%20hunger%20classification%20of%20lates%20calcarifer.pdf 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 https://www.sciencedirect.com/science/article/pii/S0168169919305332 10.1016/j.compag.2019.104883
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	SH Aquaculture. Fisheries. Angling
spellingShingle	SH Aquaculture. Fisheries. Angling Mohd Razman, Mohd Azraai Susto, Gian Antonio Cenedese, Angelo Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Abdul Ghani, Ahmad Shahrizan Adnan, Faiez Azizi Ismail, Khairul Muttaqin Taha, Zahari 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 0.5 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.
format	Article
author	Mohd Razman, Mohd Azraai Susto, Gian Antonio Cenedese, Angelo Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Abdul Ghani, Ahmad Shahrizan Adnan, Faiez Azizi Ismail, Khairul Muttaqin Taha, Zahari Mukai, Yukinori
author_facet	Mohd Razman, Mohd Azraai Susto, Gian Antonio Cenedese, Angelo Abdul Majeed, Anwar P.P. Musa, Rabiu Muazu Abdul Ghani, Ahmad Shahrizan Adnan, Faiez Azizi Ismail, Khairul Muttaqin Taha, Zahari Mukai, Yukinori
author_sort	Mohd Razman, Mohd Azraai
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. A
publishDate	2019
url	http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/ http://irep.iium.edu.my/75971/1/hunger%20classification%20azraai%202019.pdf http://irep.iium.edu.my/75971/7/Scopus%20-%20hunger%20classification%20of%20lates%20calcarifer.pdf
first_indexed	2023-09-18T21:47:26Z
last_indexed	2023-09-18T21:47:26Z
_version_	1777413543919878144

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

Similar Items