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Opis

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.

Szczegóły książki

Kategoria Książki po angielsku Earth sciences, geography, environment, planning The environment Conservation of the environment

• Pełny tytuł: Machine Learning in Aquaculture
• Podtytuł: Hunger Classification of Lates calcarifer
• Autor: Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian Antonio Susto, Yukinori Mukai
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 60
• EAN: 9789811522369
• ID: 25246602
• Wydawca: Springer Verlag, Singapore
• Waga: 454 g
• Wymiary: 235 × 155 × 5 mm
• Data wydania: 04. January 2020

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