Machine Learning in Aquaculture [[electronic resource] ] : 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

• Autore: Mohd Razman Mohd Azraai
• 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
• 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
• Serie: SpringerBriefs in Applied Sciences and Technology, . 2191-530X