Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer

Machine Learning in Aquaculture: Hunger Classification of Lates Calcarifer

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

Paperback

Series: Springerbriefs in Applied Sciences and Technology

Technology & EngineeringGeneral ComputersZoology

ISBN10: 9811522367
ISBN13: 9789811522369
Publisher: Springer Nature
Published: Jan 4 2020
Pages: 60
Weight: 0.24
Height: 0.14 Width: 6.14 Depth: 9.21
Language: English

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.

Also in

Zoology

View All