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Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

  • Mohd Azraai Mohd Razman
  • Anwar P. P. Abdul Majeed
  • Rabiu Muazu Musa
  • Zahari Taha
  • Gian-Antonio Susto
  • Yukinori Mukai
Book

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 1-9
  3. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 11-24
  4. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 25-36
  5. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 37-47
  6. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 49-57
  7. Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
    Pages 59-60

About this book

Introduction

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.

Keywords

Hunger behaviour of fish Image processing module Fish growth Computer vision Motion tracking Machine learning classifiers Artificial intelligence Fish behaviour Prediction classifiers Fish farming

Authors and affiliations

  • Mohd Azraai Mohd Razman
    • 1
  • Anwar P. P. Abdul Majeed
    • 2
  • Rabiu Muazu Musa
    • 3
  • Zahari Taha
    • 4
  • Gian-Antonio Susto
    • 5
  • Yukinori Mukai
    • 6
  1. 1.Manufacturing & Mechatronics Eng. TechUniversiti Malaysia PahangPekanMalaysia
  2. 2.Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekan, Pahang Darul MakmurMalaysia
  3. 3.Universiti Malaysia TerengganuTerengganuMalaysia
  4. 4.Manufacturing & Mechatronics Eng TechUniversiti Malaysia PahangPekanMalaysia
  5. 5.Department of Information EngineeringUniversity of PaduaPadovaItaly
  6. 6.Department of Marine ScienceInternational Islamic University MalaysiKuantanMalaysia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-15-2237-6
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
  • Publisher Name Springer, Singapore
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-981-15-2236-9
  • Online ISBN 978-981-15-2237-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site