| 書(shū)目名稱 | Machine Learning in Aquaculture |
| 副標(biāo)題 | Hunger Classificatio |
| 編輯 | Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Y |
| 視頻video | http://file.papertrans.cn/621/620657/620657.mp4 |
| 概述 | Highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques.Discusses the underlying factors that contribute |
| 叢書(shū)名稱 | SpringerBriefs in Applied Sciences and Technology |
| 圖書(shū)封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | Hunger behaviour of fish; Image processing module; Fish growth; Computer vision; Motion tracking; Machine |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-15-2237-6 |
| isbn_softcover | 978-981-15-2236-9 |
| isbn_ebook | 978-981-15-2237-6Series ISSN 2191-530X Series E-ISSN 2191-5318 |
| issn_series | 2191-530X |
| copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 |
|Archiver|手機(jī)版|小黑屋|
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