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Machine Learning in Aquaculture : Hunger Classification of Lates calcarifer / / by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai



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Autore: Mohd Razman Mohd Azraai Visualizza persona
Titolo: Machine Learning in Aquaculture : Hunger Classification of Lates calcarifer / / by Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai Visualizza cluster
Pubblicazione: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Edizione: 1st ed. 2020.
Descrizione fisica: 1 online resource (64 pages)
Disciplina: 006.31
Soggetto topico: Wildlife
Fish
Computational intelligence
Computer simulation
Signal processing
Image processing
Speech processing systems
Fish & Wildlife Biology & Management
Computational Intelligence
Simulation and Modeling
Signal, Image and Speech Processing
Persona (resp. second.): P. P. Abdul MajeedAnwar
Muazu MusaRabiu
TahaZahari
SustoGian-Antonio
MukaiYukinori
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Machine Learning in Aquaculture  Visualizza cluster
ISBN: 981-15-2237-5
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910373937903321
Lo trovi qui: Univ. Federico II
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Serie: SpringerBriefs in Applied Sciences and Technology, . 2191-530X