國立虎尾科技大學 |

Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning in Aquaculture/ by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai.
Reminder of title:	Hunger Classification of Lates calcarifer /
Author:	Mohd Razman, Mohd Azraai.
other author:	P. P. Abdul Majeed, Anwar.
Description:	VI, 60 p.online resource. :
Contained By:	Springer Nature eBook
Subject:	Wildlife. -
Online resource:	https://doi.org/10.1007/978-981-15-2237-6
ISBN:	9789811522376

Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /
Mohd Razman, Mohd Azraai.

Machine Learning in AquacultureHunger Classification of Lates calcarifer /[electronic resource] :by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai. - 1st ed. 2020. - VI, 60 p.online resource. - SpringerBriefs in Applied Sciences and Technology,2191-530X. - SpringerBriefs in Applied Sciences and Technology,.

1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour.

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.

ISBN: 9789811522376

Standard No.: 10.1007/978-981-15-2237-6doiSubjects--Topical Terms:

1254961
Wildlife.

LC Class. No.: QL81.5-84.7

Dewey Class. No.: 597

Machine Learning in Aquaculture = Hunger Classification of Lates calcarifer /
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