► Description: |
his 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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