Cover Image

Machine learning in aquaculture: hunger classification of Lates calcarifer

This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish f...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohd Razman, Mohd Azraai, P. P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, Taha, Zahari, Susto, Gian-Antonio, Mukai, Yukinori
Format: Book
Language:English
Published: Springer Singapore 2020
Subjects:
SH151 Aquaculture - Fish Culture
Online Access:http://irep.iium.edu.my/80177/1/80177_Machine%20learning%20in%20aquaculture.pdf
http://irep.iium.edu.my/80177/
https://www.springer.com/gp/book/9789811522369#aboutBook
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.80177
record_format dspace
spelling my.iium.irep.801772020-07-20T01:08:52Z http://irep.iium.edu.my/80177/ Machine learning in aquaculture: hunger classification of Lates calcarifer Mohd Razman, Mohd Azraai P. P. Abdul Majeed, Anwar Musa, Rabiu Muazu Taha, Zahari Susto, Gian-Antonio Mukai, Yukinori SH151 Aquaculture - Fish Culture This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour Springer Singapore 2020 Book PeerReviewed application/pdf en http://irep.iium.edu.my/80177/1/80177_Machine%20learning%20in%20aquaculture.pdf Mohd Razman, Mohd Azraai and P. P. Abdul Majeed, Anwar and Musa, Rabiu Muazu and Taha, Zahari and Susto, Gian-Antonio and Mukai, Yukinori (2020) Machine learning in aquaculture: hunger classification of Lates calcarifer. Springer Singapore, Singapore. ISBN 978-981-15-2236-9 https://www.springer.com/gp/book/9789811522369#aboutBook 10.1007/978-981-15-2237-6
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic SH151 Aquaculture - Fish Culture
spellingShingle SH151 Aquaculture - Fish Culture
Mohd Razman, Mohd Azraai
P. P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Taha, Zahari
Susto, Gian-Antonio
Mukai, Yukinori
Machine learning in aquaculture: hunger classification of Lates calcarifer
description This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour
format Book
author Mohd Razman, Mohd Azraai
P. P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Taha, Zahari
Susto, Gian-Antonio
Mukai, Yukinori
author_facet Mohd Razman, Mohd Azraai
P. P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Taha, Zahari
Susto, Gian-Antonio
Mukai, Yukinori
author_sort Mohd Razman, Mohd Azraai
title Machine learning in aquaculture: hunger classification of Lates calcarifer
title_short Machine learning in aquaculture: hunger classification of Lates calcarifer
title_full Machine learning in aquaculture: hunger classification of Lates calcarifer
title_fullStr Machine learning in aquaculture: hunger classification of Lates calcarifer
title_full_unstemmed Machine learning in aquaculture: hunger classification of Lates calcarifer
title_sort machine learning in aquaculture: hunger classification of lates calcarifer
publisher Springer Singapore
publishDate 2020
url http://irep.iium.edu.my/80177/1/80177_Machine%20learning%20in%20aquaculture.pdf
http://irep.iium.edu.my/80177/
https://www.springer.com/gp/book/9789811522369#aboutBook
_version_ 1674065929625927680
score 13.211869

Similar Items