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Machine learning in aquaculturehunge...

Mohd Razman, Mohd Azraai.

Machine learning in aquaculturehunger classification of Lates calcarifer /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in aquacultureby Mohd Azraai Mohd Razman ... [et al.].
Reminder of title:	hunger classification of Lates calcarifer /
other author:	Mohd Razman, Mohd Azraai.
Published:	Singapore :Springer Singapore :2020.
Description:	vi, 60 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	FishesFeeding and feeds
Online resource:	https://doi.org/10.1007/978-981-15-2237-6
ISBN:	9789811522376$q(electronic bk.)

Machine learning in aquaculturehunger classification of Lates calcarifer /
Machine learning in aquaculturehunger classification of Lates calcarifer /[electronic resource] :by Mohd Azraai Mohd Razman ... [et al.]. - Singapore :Springer Singapore :2020. - vi, 60 p. :ill., digital ;24 cm. - 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$q(electronic bk.)

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

863566
Fishes
--Feeding and feeds

LC Class. No.: SH156 / .M643 2020

Dewey Class. No.: 597

Machine learning in aquaculturehunger classification of Lates calcarifer /
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505 0 $a 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.
520 $a 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.
650 0 $a Fishes $x Feeding and feeds $x Data processing. $3 863566
650 0 $a Machine learning. $3 188639
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650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Signal, Image and Speech Processing. $3 273768
700 1 $a Mohd Razman, Mohd Azraai. $3 863565
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