Artificial Vision in Aquaculture 4.0: Detection and Kinematic Tracking of Biomass Using Single-Stage Neural Networks
Main Article Content
Abstract
La visión artificial y los sistemas inteligentes representan una tendencia clave para automatizar el monitoreo conductual en la acuicultura intensiva. Sin embargo, los enfoques comerciales actuales suelen ser costosos, rígidos y dependientes de la observación humana manual. El objetivo de este trabajo, fue desarrollar, entrenar y validar el sistema denominado EBISU, un marco de software no invasivo para evaluar variables cinemáticas en vivo. Metodología: La investigación es de tipo tecnológica aplicada; se empleó una muestra experimental de tilapia (Oreochromis niloticus) en un entorno de recirculación controlado. Como instrumentos y técnicas se utilizaron cámaras digitales acopladas a nodos IoT, procesamiento de video en paralelo mediante PyQt5 y la arquitectura de aprendizaje profundo YOLO para la detección y seguimiento geométrico cuadro por cuadro. Resultados: Los resultados demostraron que el sistema procesa flujos de video fluidamente, determinando con precisión si las velocidades de nado indican parámetros normales o estrés metabólico. Discusión: Se destaca queEBISU supera la sustracción de fondo clásica al mitigar eficazmente reflejos lumínicos y turbulencias del agua. Conclusión: El sistema automatiza con éxito la supervisión biológica continua, reduciendo el error humano y sentando bases metodológicas para futuros trabajos en acuicultura de precisión en tiempo real.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Material appearing in the journal may be reproduced and cited, provided that it complies with the conditions established in the licenses of the published articles Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Ahmed, M. S., & Jeba, S. M. (2024). SalmonScan: A novel image dataset for machine learning and deep learning analysis in fish disease detection in aquaculture. Data in Brief, 54, 110388. https://doi.org/10.1016/j.dib.2024.110388
Al-Abri, S., Keshvari, S., Al-Rashdi, K., Al-Hmouz, R., & Bourdoucen, H. (2025). Computer vision-based approaches for fish monitoring systems: a comprehensive study. Artificial Intelligence Review, 58(6), 185. https://doi.org/10.1007/s10462-025-11180-3
Biazi, V., & Marques, C. (2023). Industry 4.0-based smart systems in aquaculture: A comprehensive review. Aquacultural Engineering, 103, 102360. https://doi.org/10.1016/j.aquaeng.2023.102360
Cai, Y., Yao, Z., Jiang, H., Qin, W., Xiao, J., Huang, X., ... & Feng, H. (2024). Rapid detection of fish with SVC symptoms based on machine vision combined with a NAM-YOLO v7 hybrid model. Aquaculture, 582, 740558. https://doi.org/10.1016/j.aquaculture.2024.740558
Cui, M., Liu, X., Liu, H., Zhao, J., Li, D., & Wang, W. (2025). Fish tracking, counting, and behavior analysis in digital aquaculture: A comprehensive survey. Reviews in Aquaculture, 17(1), e13001. https://doi.org/10.1111/raq.13001Digital Object Identifier (DOI)
Fitzgerald, A., Ioannou, C. C., Consuegra, S., Dowsey, A., & García de Leaniz, C. (2025). Machine vision applications for welfare monitoring in aquaculture: challenges and opportunities. Aquaculture, Fish and Fisheries, 5(1), e70036. https://doi.org/10.1002/aff2.70036
García, L. V. (2024). Computer vision simulator for the detection, tracking, and distance calculation of moving objects. European Public & Social Innovation Review, 9, 1–16. https://doi.org/10.31637/epsir-2024-812
He, Q., Yu, H., Qin, H., Mei, Y., Xu, L., Chai, Y., ... & Chen, Y. (2026). Deep learning-based computer vision for fish behavior recognition in intensive aquaculture: A comprehensive review. Computer Science Review, 60, 100896. https://doi.org/10.1016/j.cosrev.2026.100896
Li, X., Zhao, S., Chen, C., Cui, H., Li, D., & Zhao, R. (2024). YOLO-FD: An accurate fish disease detection method based on multi-task learning. Expert Systems with Applications, 258, 125085. https://doi.org/10.1016/j.eswa.2024.125085
Liu, C., Wang, Z., Li, Y., Zhang, Z., Li, J., Xu, C., ... & Duan, Q. (2023). Research progress of computer vision technology in abnormal fish detection. Aquacultural Engineering, 103, 102350. https://doi.org/10.1016/j.aquaeng.2023.102350
Ranjan, R. (2026). YOLO in Precision Aquaculture: A Decadal Bibliometric and Systematic Review of Applications, Architectural Adaptations, and Deployment Challenges. Journal of Agriculture and Food Research, 102982. https://doi.org/10.1016/j.jafr.2026.102982
Rivadeneira, F., Yi, E. A. C., Miyahira, A., Zinanyuca, M., & Cuellar, F. (2024). Comparative evaluation of YOLO models for gauge detection. In 2024 Latin American Robotics Symposium (LARS) (pp. 1–5). IEEE. https://doi.org/ 10.1109/LARS64411.2024.10786467
Sun, Y., Liu, P., Bakari?, M. B., Yu, J., Kong, C., & Zhang, X. (2025). Contact and non-contact physiological stress indicators in aquatic models: A review. Aquaculture, 596, 741830. https://doi.org/10.1016/j.aquaculture.2024.741830
Terven, J., Córdova-Esparza, D. M., & Romero-González, J. A. (2023). A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680–1716. https://doi.org/10.3390/make5040083
Zhang, S., Li, D., Zhao, J., Yao, M., Chen, Y., Huo, Y., ... & Wang, H. (2025). Research advances on fish feeding behavior recognition and intensity quantification methods in aquaculture. arXiv e-prints, arXiv-2502. https://doi.org/10.48550/arXiv.2502.15311