Behavioral spatial-temporal characteristics-based appetite assessment for fish school in recirculating aquaculture systems

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.