ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
This article is part of the Research TopicAI-Driven Scientific Discovery: Transforming Research Across DisciplinesView all 6 articles
Quantification of feeding intensity and feeding control of largemouth bass based on water surface vibration characteristics
Provisionally accepted
- Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences (CAFS), Beijing, China
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In response to the demand for precise feeding in high-density aquaculture, this study established a dynamic prediction model for fish feeding intensity by integrating vibration signal quantification and deep learning. Through multidimensional experiments (fish size: 50-300 g; stocking density: 20-60 fish/group; feeding speed: 1-3g/s; feed particle size: 2#4#6#), we quantified the three-axis displacement signals of Micropterus salmoides during feeding. Results demonstrated significant effects of all parameters on water surface fluctuations (P < 0.05). Vibration displacement exhibited linear relationships with fish size and density. The 300 g group showed 37.2% higher peak amplitude than the 50 g group, while the 60-fish density group exceeded the 20-fish group by 41.3%. Optimal palatability (4#) reduced fluctuation frequency by 42%. A predictive model for feeding vibration patterns was developed, incorporating fish size (S), density (D), feeding speed (V), feed particle size (Φ), real-time triaxial vibration sum, and time series (t) as inputs to predict the summed vibration displacement at t+5 s, which serves as a quantitative proxy for feeding intensity. The Long Short-Term Memory (LSTM) model accurately captured fish feeding dynamics (RMSE = 69.43 µm, MAE = 48.00 µm, R² = 0.883). In comparative analysis, the LSTM outperformed Gated Recurrent Unit (GRU) and Transformer models in forecasting accuracy. Deployed on an embedded system (Orange Pi AiPRO), closed-loop tests demonstrated superior performance: residual feed rates were ≤ 0.8% across all trials, outperforming optical flow (2.69% residuals) and graph neural network (6.58% residuals) methods. The space complexity of the vibration-LSTM approach was only 6.4-31.8% of GCN-based approaches, enabling cost-effective (< $200) real-time control.
Keywords: Aquaculture, fish appetite quantification, fish feeding model, fluctuations in fish feeding, onlineintelligent feeding
Received: 29 Jun 2025; Accepted: 26 Jan 2026.
Copyright: © 2026 Zhang, Liu, Zhang, Ni, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Andong Liu
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