Towards Precision Feeding Using Behavioral Monitoring in Marine Cages

TL;DR

This paper presents a real-time AI-based monitoring system using computer vision to analyze fish behavior in marine cages, aiming to optimize feeding practices for sustainable aquaculture.

Contribution

It introduces a novel AI-driven system that assesses feeding behavior and satiation levels in sea bass, addressing a key gap in precision feeding for marine aquaculture.

Findings

Distinct behavior patterns linked to feeding levels

Imbalances in activity indicate overfeeding or underfeeding

Potential for predicting satiation and controlling feeding duration

Abstract

Aquaculture is expected to account for two-thirds of global fish consumption by 2030, highlighting the need for sustainable and efficient practices. Feeding is crucial to aquaculture success, influenced by factors like fish size, environment, and health. This study addresses a gap in feeding control for sea cages by developing a real-time monitoring system, using AI models and computer vision to analyze feeding behavior with European sea bass as pilot species. Key metrics like fish speed and a new feeding behavior index (FBI) were used to assess responses to different feeding scenarios. The results revealed distinct behavior patterns based on feeding quantity, with imbalances in activity when fish are overfed or underfed. The results can be used for predicting the level of satiation of the fish and controlling feeding duration.