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.