Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer

By Mohd Azraai Mohd RazmanZahari TahaYukinori Mukai, et al.

1. Introduction

1.1 Fish hunger behaviour
1.2 Image processing parameters
1.3 Machine learning in fish behaviour

2.0 Monitoring and feeding integration of demand feeder systems

2.1 Overview
2.2 Fish monitoring setup
2.3 Fish Growth
2.4 Results and discussion

3. Image processing features extraction on fish behaviour

3.1 Overview
3.2 Data pre-processing
3.3 Clustering
3.4 Feature extraction and classification
3.5 Results and discussion

4. Time-series identification of fish feeding behaviour

4.1 Overview
4.2 Event identification
4.3 PCA based feature selection
4.4 Classification accuracy
4.5 Results and discussion

5. Hyperparameter tuning for hunger state classification

5.1 Overview
5.2 Optimization of Bayesian
5.3 Classification accuracy
5.4 Result and discussion

6.0 Concluding remarks

• Format: Paperback
• Language: English
• ISBN: 9811522367
• ISBN13: 9789811522369
• Release Date: January 2020
• Publisher: Springer
• Length: 60 Pages
• Weight: 0.24 lbs.
• Dimensions: 0.1" x 6.1" x 9.2"