Name: Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer (Springerbriefs in Applied Sciences and Technology) (en Inglés)
Price: 236765.00 COP
Availability: InStock
Author: Mohd Azraai Mohd Razman; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa
ISBN: 9789811522369

portada Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer (Springerbriefs in Applied Sciences and Technology) (en Inglés)

Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer (Springerbriefs in Applied Sciences and Technology) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Mohd Azraai Mohd Razman; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa

Año

2020

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9789811522369

N° edición

Categorías

Modelos y simulaciones por ordenador

Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer (Springerbriefs in Applied Sciences and Technology) (en Inglés)

Mohd Azraai Mohd Razman; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa (Autor) · Springer · Tapa Blanda

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Reseña del libro "Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer (Springerbriefs in Applied Sciences and Technology) (en Inglés)"

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.