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...
| Main Authors: | , , , , , |
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| Format: | Book |
| Language: | English |
| Published: |
Springer Singapore
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/80177/ http://irep.iium.edu.my/80177/1/80177_Machine%20learning%20in%20aquaculture.pdf |
| _version_ | 1848788914532253696 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-14T17:48:24Z |
| format | Book |
| id | iium-80177 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:48:24Z |
| publishDate | 2020 |
| publisher | Springer Singapore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-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 |
| 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 |
| title | 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_short | Machine learning in aquaculture: hunger classification of Lates calcarifer |
| title_sort | machine learning in aquaculture: hunger classification of lates calcarifer |
| topic | SH151 Aquaculture - Fish Culture |
| url | http://irep.iium.edu.my/80177/ http://irep.iium.edu.my/80177/ http://irep.iium.edu.my/80177/ http://irep.iium.edu.my/80177/1/80177_Machine%20learning%20in%20aquaculture.pdf |