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Machine learning in aquaculture = hu...

Mohd Razman, Mohd Azraai.

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Machine learning in aquaculture = hunger classification of Lates calcarifer /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in aquaculture/ by 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.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Fishes - Feeding and feeds -
Online resource:	https://doi.org/10.1007/978-981-15-2237-6
ISBN:	9789811522376

Machine learning in aquaculture = hunger 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

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

3446042
Fishes
--Feeding and feeds

LC Class. No.: SH156 / .M643 2020

Dewey Class. No.: 597

Machine learning in aquaculture = hunger classification of Lates calcarifer /
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245 0 0 $a Machine learning in aquaculture $h [electronic resource] : $b hunger classification of Lates calcarifer / $c by Mohd Azraai Mohd Razman ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a vi, 60 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
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 3446042
650 0 $a Machine learning. $3 533906
650 1 4 $a Fish & Wildlife Biology & Management. $3 898129
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Mohd Razman, Mohd Azraai. $3 3446041
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in applied sciences and technology. $3 1565541
856 4 0 $u https://doi.org/10.1007/978-981-15-2237-6
950 $a Biomedical and Life Sciences (Springer-11642)