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| 008 |
200224 ||| eng |
| 020 |
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|a 9789811522376
|
| 100 |
1 |
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|a Mohd Razman, Mohd Azraai
|
| 245 |
0 |
0 |
|a Machine Learning in Aquaculture
|h Elektronische Ressource
|b Hunger Classification of Lates calcarifer
|c by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai
|
| 250 |
|
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|a 1st ed. 2020
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| 260 |
|
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|a Singapore
|b Springer Nature Singapore
|c 2020, 2020
|
| 300 |
|
|
|a VI, 60 p
|b online resource
|
| 505 |
0 |
|
|a 1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour
|
| 653 |
|
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|a Computational intelligence
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| 653 |
|
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|a Computer simulation
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| 653 |
|
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|a Computer Modelling
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| 653 |
|
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|a Computational Intelligence
|
| 653 |
|
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|a Animal culture
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| 653 |
|
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|a Signal, Speech and Image Processing
|
| 653 |
|
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|a Animal Science
|
| 653 |
|
|
|a Signal processing
|
| 700 |
1 |
|
|a P. P. Abdul Majeed, Anwar
|e [author]
|
| 700 |
1 |
|
|a Muazu Musa, Rabiu
|e [author]
|
| 700 |
1 |
|
|a Taha, Zahari
|e [author]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a SpringerBriefs in Applied Sciences and Technology
|
| 028 |
5 |
0 |
|a 10.1007/978-981-15-2237-6
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-15-2237-6?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 636
|
| 520 |
|
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|a 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
|