New hearing for fish: AI unravels underwater feeding noises
Researchers in Israel have taken a notable step in aquaculture science by teaching computers to listen to what fish say when they eat. Using artificial intelligence to decode the sounds produced by fish during feeding, the team aims to enable real-time monitoring, earlier disease detection, and more precise feeding in aquaculture ponds. The study, to be presented at an international conference, suggests that the aquatic world may be more communicative about its health and needs than previously thought.
How the learning process works
Traditional aquaculture relies on visual cues and routine feed schedules. The new approach adds an acoustic layer. Microphones capture the underwater feeding noises, while machine-learning models analyze patterns associated with feeding bouts, hunger signals, and potential distress signals linked to disease or suboptimal water quality. The researchers trained the AI on labeled recordings, teaching it to distinguish normal feeding sounds from anomalies that could indicate stress or illness. In practice, the system can flag unusual sound patterns in near real-time, enabling farm managers to intervene before problems escalate.
The science behind “sound feeding”
Fish produce a variety of noises during feeding, including clip-like sounds from jaw movements, gulping, and agitation around feeding sites. These noises are influenced by species, tank or pond geometry, water temperature, and feeding method. By correlating acoustic data with health and growth metrics, scientists hope to build a robust acoustic fingerprint for healthy feeding, early disease indicators, and optimal feeding rates. The Israeli study emphasizes scalable AI techniques that can operate in real-world pond environments rather than controlled laboratories.
Potential benefits for aquaculture
- Real-time monitoring: Continuous acoustic surveillance can reveal feeding efficiency, stress, or irregularities as they happen.
- Early disease detection: Subtle changes in sound patterns may precede visible symptoms, allowing proactive management.
- Precision feeding: By aligning feed release with actual digestion cues, farmers can reduce waste, lower costs, and minimize environmental impact.
- Animal welfare: Non-invasive listening provides a non-stressful way to assess fish well-being on a large scale.
Practical considerations and challenges
Applying acoustic AI in commercial ponds faces several hurdles. Variability in species, pond size, and water quality requires adaptable models trained on diverse datasets. Acoustic data demands robust filtering to separate fish sounds from ambient noise such as aerators and weather. Data privacy and processing power are practical concerns for smaller operations. Nonetheless, the growing field of underwater acoustics and AI-driven analytics is rapidly maturing, with pilot programs showing promise across different farming contexts.
What this means for the future of farming
The Israeli research aligns with a broader push toward sensor-driven farming in which data from sound, temperature, and imaging informs decisions. If AV-based or hydroacoustic monitoring becomes mainstream, farms could see improvements in feed conversion ratios, reduced mortality, and more resilient stock. The study’s authors note that collaboration with veterinarians, fish biologists, and technology developers will be crucial to translate acoustic signals into actionable insights for managers and consumers alike.
Concluding thoughts
Listening to fish might seem like a novel concept, but it represents a natural extension of precision agriculture into the aquatic realm. As AI continues to listen, learn, and interpret underwater feeding noises, the industry could experience a quieter, more efficient, and healthier future for farmed fish—and for the ecosystems that support them.
