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Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

By: Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Zahari Taha,Gian-Antonio Susto,Yukinori Mukai

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Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

By: Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Zahari Taha,Gian-Antonio Susto,Yukinori Mukai

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.

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Page Count

64

Publisher

Springer Nature

Published Date

ISBN 10

9811522375

ISBN 13

9789811522376

Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

By: Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Zahari Taha,Gian-Antonio Susto,Yukinori Mukai

Write a review Read reviews Take a Quiz Solve Book Puzzle

Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

By: Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Zahari Taha,Gian-Antonio Susto,Yukinori Mukai

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

64

Publisher

Springer Nature

Published Date

ISBN 10

9811522375

ISBN 13

9789811522376