Machine Learning in Aquaculture - Hunger Classification of Lates calcarifer (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs in Applied Sciences and Technology
Release date:	2020
First published:	2020
Authors:	Mohd Azraai Mohd Razman • Anwar P.P. Abdul Majeed • Rabiu Muazu Musa • Zahari Taha • Gian-Antonio Susto • Yukinori Mukai
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	60
Edition:	1st ed. 2020
ISBN-13:	978-981-15-2236-9
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Earth & environment > The environment > Conservation of the environment > Conservation of wildlife & habitats > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	981-15-2236-7
Barcode:	9789811522369

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