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Machine Learning in Aquaculture

- Hunger Classification of Lates calcarifer
Af: Mohd Azraai Mohd Razman, Zahari Taha, Yukinori Mukai, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Gian-Antonio Susto Engelsk Paperback

Machine Learning in Aquaculture

- Hunger Classification of Lates calcarifer
Af: Mohd Azraai Mohd Razman, Zahari Taha, Yukinori Mukai, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Gian-Antonio Susto Engelsk Paperback
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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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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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Produktdetaljer
Sprog: Engelsk
Sider: 60
ISBN-13: 9789811522369
Indbinding: Paperback
Udgave:
ISBN-10: 9811522367
Kategori: Elektronik teknik
Udg. Dato: 4 jan 2020
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 4 jan 2020
Forfatter(e): Mohd Azraai Mohd Razman, Zahari Taha, Yukinori Mukai, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Gian-Antonio Susto
Forfatter(e) Mohd Azraai Mohd Razman, Zahari Taha, Yukinori Mukai, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Gian-Antonio Susto

Kategori Elektronik teknik

ISBN-13 9789811522369

Sprog Engelsk

Indbinding Paperback

Sider 60

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 4 jan 2020

Oplagsdato 4 jan 2020

Forlag Springer Verlag, Singapore

Kategori sammenhænge