Machine learning in aquaculture: hunger classification of Lates calcarifer

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

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Abstract

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

Item Type:	Book
Additional Information:	6616/80177
Uncontrolled Keywords:	Machine learning, aquaculture, hunger classification, Lates calcarifer
Subjects:	S Agriculture > SH Aquaculture. Fisheries. Angling > SH151 Aquaculture - Fish Culture
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:	20 Jul 2020 09:08
Last Modified:	20 Jul 2020 09:08
URI:	http://irep.iium.edu.my/id/eprint/80177

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