Behavioral spatial-temporal characteristics-based appetite assessment for fish school in recirculating aquaculture systems
文献类型： 外文期刊
作者： Wei, Dan 1 ; Bao, Encai 2 ; Wen, Yanci 1 ; Zhu, Songming 1 ; Ye, Zhangying 1 ; Zhao, Jian 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310000, Peoples R China
2.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Key Lab Agr Engn Middle & Lower Reaches Yangtze R, Minist Agr, Nanjing 210014, Jiangsu, Peoples R China
3.Zhejiang Univ, Ocean Acad, Zhoushan 316000, Peoples R China
关键词： Appetite assessment; Spatial-temporal characteristics; Modified kinetic energy; Customized recurrent neural network; Recirculating aquaculture system
期刊名称：AQUACULTURE （ 影响因子：5.135； 五年影响因子：5.125 ）
ISSN： 0044-8486
年卷期： 2021 年 545 卷
页码：
收录情况： SCI
摘要： Knowing precise fish appetite is a prerequisite for developing a high-efficient feeding system in aquaculture. However, the current studies on the assessment of fish appetite mostly focus on relevant spatial features of fish school, ignoring the time series-based variation characteristics in the process of fish feeding, which may decrease the accuracy of appetite assessment. To address the research gap and solve these problems, a novel and efficient fish appetite grading method, based on the spatial-temporal characteristics of fish behavior, was proposed in this study, using the modified kinetic energy model and customized recurrent neural network. First, the modified kinetic energy model was used to quantify and extract the behavioral spatial characteristics of fish school without foreground segmentation and individual tracking. The temporal features of fish feeding behavior were learned based on the vector sequence of spatial characteristics above, by means of a customized recurrent neural network. Following this, fish appetite level was determined with the help of layers of full connection and soft max. Through the exhaustive test on four different behavior datasets, the presented method shows better performance (accuracy: 97.08%, 97.35%, 92.50%, 98.31%, respectively) on appetite assessment of fish than many other state-of-the-art methods.
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