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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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