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.
| Autorius: | Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Yukinori Mukai, Zahari Taha, Gian-Antonio Susto, Rabiu Muazu Musa, |
| Serija: | SpringerBriefs in Applied Sciences and Technology |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 68 |
| ISBN-10: | 9811522367 |
| ISBN-13: | 9789811522369 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Electronics engineering |
Parašykite atsiliepimą apie „Machine Learning in Aquaculture: Hunger Classification of Lates calcarifer“
