Visión artificial en Acuicultura 4.0: Detección y seguimiento cinemático de biomasa mediante redes neuronales de una sola etapa
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Computer vision and intelligent systems represent a key trend for automating behavioral monitoring in intensive aquaculture. However, current commercial approaches tend to be costly, inflexible, and dependent on manual human observation. The objective of this study was to develop, train, and validate the system known as EBISU, a non-invasive software framework for evaluating kinematic variables in real time. Methodology: This research is of an applied technological nature; an experimental sample of tilapia (Oreochromis niloticus) was used in a controlled recirculating aquaculture system. The instruments and techniques employed included digital cameras connected to IoT nodes, parallel video processing using PyQt5, and the YOLO deep learning architecture for frame-by-frame geometric detection and tracking. Results: The results demonstrated that the system processes video streams smoothly, accurately determining whether swimming speeds indicate normal parameters or metabolic stress. Discussion: It is noteworthy that EBISU outperforms classical background subtraction by effectively mitigating light reflections and water turbulence. Conclusion: The system successfully automates continuous biolog l monitoring, reducing human error and laying the methodological groundwork for future work in real-time precision aquaculture.
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