Machine Learning in Aquaculture: Hunger Classification of Lates calcarifer

By Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa and 3 more

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.

• Language: English
• Publisher: Springer
• Release date: Jan 2, 2020
• ISBN: 9789811522376

Book preview

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

Machine Learning in Aquaculture

Hunger Classification ofLates calcarifer

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Mohd Azraai Mohd Razman

Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

Anwar P. P. Abdul Majeed

Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

Rabiu Muazu Musa

Centre for Fundamental and Continuing Education, Department of Credited Co-curriculum, Universiti Malaysia Terengganu, Terengganu, Malaysia

Zahari Taha

Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

Gian-Antonio Susto

Department of Information Engineering, University of Padua, Padua, Italy

Yukinori Mukai

Department of Marine Science, International Islamic University Malaysia, Kuantan, Malaysia

ISSN 2191-530Xe-ISSN 2191-5318

SpringerBriefs in Applied Sciences and Technology

ISBN 978-981-15-2236-9e-ISBN 978-981-15-2237-6

https://doi.org/10.1007/978-981-15-2237-6

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

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Contents

1 Introduction 1

1.​1 Overview 1

1.​2 Fish Hunger Behaviour 3

1.​3 Image Processing Parameter 4

1.​4 Machine Learning in Fish Behaviour 6

References 7

2 Monitoring and Feeding Integration of Demand Feeder Systems 11

2.​1 Overview 11

2.​2 Fish Monitoring Set-Up 12

2.​2.​1 Experimental Materials and Set-Up 13

2.​2.​2 Image Processing Data Extraction 17

2.​3 Fish Growth 19

2.​4 Results and Discussion 21

2.​5 Summary 23

References 24

3 Image Processing Features Extraction on Fish Behaviour 25

3.​1 Overview 25

3.​2 Data Pre-processing 26

3.​3 Clustering 27

3.​4 Feature Selection and Classification 27

3.​4.​1 Boxplot Analysis 28

3.​4.​2 Principal Component Analysis (PCA) 28

3.​4.​3 Machine Learning Classifiers 29

3.​5 Results and Discussion 31

3.​6 Summary 34

References 35

4 Time-Series Identification on Fish Feeding Behaviour 37

4.​1 Overview 37

4.​2 Event Identification 38

4.​3 Features Selection PCA-Based 39

4.​4 Classification Accuracy 39

4.​5 Results and Discussion 41

4.​6 Summary 45

References 46

5 Hyperparameter Tuning of the Model for Hunger State Classification 49

5.​1 Overview 49

5.​2 Optimization of Bayesian 50

5.​3 Classification Accuracy 50

5.​4 Results and Discussion 52

5.​5 Summary 56

References 56

6 Concluding Remarks 59

6.​1 Overview 59

6.​2 Main Contributions 60

6.​3 Future Work 60

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

M. A. Mohd Razman et al.Machine Learning in AquacultureSpringerBriefs in Applied Sciences and Technologyhttps://doi.org/10.1007/978-981-15-2237-6_1

1. Introduction

Mohd Azraai Mohd Razman¹  , Anwar P. P. Abdul Majeed¹  , Rabiu Muazu Musa²  , Zahari Taha¹  , Gian-Antonio Susto³   and Yukinori Mukai⁴  

(1)

Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

(2)

Centre for Fundamental and Continuing Education, Department of Credited Co-curriculum, Universiti Malaysia Terengganu, Terengganu, Malaysia

(3)

Department of Information Engineering, University of Padua, Padua, Italy

(4)

Department of Marine Science, International Islamic University Malaysia, Kuantan, Malaysia

Mohd Azraai Mohd Razman (Corresponding author)

Email: [e-mail]

Anwar P. P. Abdul Majeed

Email: [e-mail]

Rabiu Muazu Musa

Email: [e-mail]

Zahari Taha

Email: [e-mail]

Gian-Antonio Susto

Email: [e-mail]

Yukinori Mukai

Email: [e-mail]

Abstract

This chapter starts by exploring the motivation behind identifying fish hunger behaviour. The elaboration on factor triggers fish behaviour which will be explained specifically towards hunger characteristics. The implementation of technologies using image processing to extract significant parameters will be discussed. Lastly, the machine learning (ML) techniques are used in fish behaviour for classification. The outcome of this chapter is to recognize the underlining framework by combining aquaculture, engineering and artificial intelligence (AI).

Keywords

Fish hunger behaviourLates calcarifer Machine learningClassificationImage processingAquaculture

1.1 Overview

The sustainability of sustenance is very crucial more importantly, with the ever-growing population that increases food demand yearly. By managing the food supply or protein source specifically, the amount of reared fish must be kept concurrent with the resources consumed. Table 1.1 demonstrates the projection of fish captured and reared aquaculture predicted by the Malaysian National Food Agency Policy 2015–2020 [1]. The overall trend suggests that the total amount