Machine Learning in Aquaculture: Hunger Classification of Lates calcarifer (1st ed. 2020) (SpringerBriefs in Applied Sciences and Technology)
By: and and and and and
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- Synopsis
- 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.
- Copyright:
- 2020
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9789811522376
- Related ISBNs:
- 9789811522369
- Publisher:
- Springer Singapore, Singapore
- Date of Addition:
- 01/17/20
- Copyrighted By:
- The Author
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Science, Animals, Computers and Internet, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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