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1 June 2020 Visual analysis of fish feeding intensity for smart feeding in aquaculture using deep learning
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Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115150L (2020) https://doi.org/10.1117/12.2566902
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia
Abstract
This paper presents a novel deep-learning approach to analyze the fish feeding intensity based on the images of fish tanks during the fish feeding process. The grade of the fish feeding intensity is an important indicator of fish appetite. On the design of a smart feeding system in aquaculture, this information is of great significance for guiding feeding and optimizing the fish production. However, conventional fish appetite assessment methods are inefficient and subjective. To solve these problems, in this study, based on a space-time two-stream 3D CNN, a deep learning approach for grading fish feeding intensity is proposed to evaluate fish appetite. The flow of the approach is implemented as follows. First, a fixed RGB camera is setup to capture the videos from the fish tanks during the feeding processes. This also constructs a dataset for training the two-stream neural network, and the fish appetite levels are graded using the trained neural network model. Finally, the performance of the method is evaluated and compared with other CNN-based deep learning approaches. The results show that the grading accuracy reached 91.18%, which outperforms the compared CNN-based approaches. Thus, the model can be used to detect and evaluate fish appetite to guide production practices.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jui-Yuan Su, Pei-Hua Zhang, Sin-Yi Cai, Shyi-Chyi Cheng, and Chin-Chun Chang "Visual analysis of fish feeding intensity for smart feeding in aquaculture using deep learning", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115150L (1 June 2020); https://doi.org/10.1117/12.2566902
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