@article{Salako:348982,
      recid = {348982},
      author = {Salako, Joshua  and Ojo, Foluso  and Awe, Olushina Olawale  },
      title = {Fish-NET: Advancing Aquaculture Management through  AI-Enhanced Fish Monitoring and Tracking},
      journal = {AGRIS on-line Papers in Economics and Informatics},
      address = {2024-06-30},
      number = {665-2025-188},
      month = {Jun},
      year = {2024},
      abstract = {This study seeks to enhance aquaculture and fishery  management using artificial intelligence, focusing on  Nigerian catfish farming. The methodology encompasses a  sequence of steps from data collection to validation. A  dataset, primarily composed of aerial imagery from catfish  ponds and supplemented with additional data from the  internet, formed the foundation of this research. By  leveraging computer vision and deep learning techniques,  the data were processed to assess the potential of the  three distinct cutting-edge object detection models. Based  on various evaluation metrics to gauge their effectiveness  in fish detection tasks, the Faster R-CNN emerged as the  optimal model, boasting a superior balance of precision and  recall. This model was subsequently integrated with an  object-tracking model and deployed as an application,  yielding promising results in terms of fish detection and  tracking. The findings in this study suggest that AI-driven  tools can automate monitoring processes, significantly  increasing accuracy and efficiency in resource  utilization.},
      url = {http://ageconsearch.umn.edu/record/348982},
      doi = {https://doi.org/10.22004/ag.econ.348982},
}