Machine Learning in Aquaculture

Hunger Classification of Lates calcarifer

Zahari Taha, Yukinori Mukai, Anwar P. P. Abdul Majeed, et al.

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ca. 53,49 €
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Springer Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Landwirtschaft, Gartenbau

Beschreibung

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.

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Schlagwörter

Fish farming, Fish and Wildlife Biology, Artificial intelligence, Motion tracking, Computer vision, Hunger behaviour of fish, Image processing module, Prediction classifiers, Fish behaviour, Machine learning classifiers, Fish growth