A Framework for Age Estimation of Fish from Otoliths: Synergy Between RANSAC and Deep Neural Networks

This study represents a significant advancement in fish ecology by applying deep learning techniques to automate and improve the counting of growth rings in otoliths, which are essential for determining the age and growth patterns of fish. Traditionally, manual methods have been used to analyze these rings, but these approaches are time-consuming, require significant expertise, and are prone to bias. To address these limitations, we propose a novel methodology that combines convolutional neural networks (CNNs) with the RANSAC algorithm, enhancing the accuracy and reliability of ring detection, even in the presence of noise or natural image variations. Unlike manual techniques, which depend on observer expertise and subjective interpretation, our approach improves performance, often surpassing human experts while reducing analysis time. The results demonstrate the potential of deep learning and RANSAC in otolith research, offering powerful tools for sustainable fish population management and transforming research practices in marine ecology by providing faster, more reliable, and accessible analytical methods, setting new standards for more rigorous research.

@article{KONE2024,
  title     = {A Framework for Age Estimation of Fish from Otoliths: Synergy Between RANSAC and Deep Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Souleymane KONE and Abdoulaye SERE and Dekpeltaki´e Augustin METOUALE SOMDA and Jos´e Arthur OUEDRAOGO},
  doi       = {10.14569/IJACSA.2024.0151292},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151292}
}