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Machine learning in aquaculturehunge...

Mohd Razman, Mohd Azraai.

Machine learning in aquaculturehunger classification of Lates calcarifer /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in aquacultureby Mohd Azraai Mohd Razman ... [et al.].
其他題名:	hunger classification of Lates calcarifer /
其他作者:	Mohd Razman, Mohd Azraai.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	vi, 60 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	FishesFeeding and feeds
電子資源:	https://doi.org/10.1007/978-981-15-2237-6
ISBN:	9789811522376$q(electronic bk.)

Machine learning in aquaculturehunger classification of Lates calcarifer /
Machine learning in aquaculturehunger classification of Lates calcarifer /[electronic resource] :by Mohd Azraai Mohd Razman ... [et al.]. - Singapore :Springer Singapore :2020. - vi, 60 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour.

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.

ISBN: 9789811522376$q(electronic bk.)

Standard No.: 10.1007/978-981-15-2237-6doiSubjects--Topical Terms:

863566
Fishes
--Feeding and feeds

LC Class. No.: SH156 / .M643 2020

Dewey Class. No.: 597

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