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<p>Multiparametric video-based aquaculture monitoring systems are becoming essential for observing and quantifying fish behavior in real time. These platforms enable high-frequency, continuous recording of underwater activity, supporting automation in feeding management and behavioral analytics. The Tilapia Feeding Behavior Image Dataset (TFBID-mini) was generated using controlled recirculating aquaculture tanks equipped with high-definition underwater cameras to capture feeding events of Oreochromis niloticus (Nile tilapia). The dataset consists of 4,000 expert-annotated images classified into four feeding-intensity levels-None, Weak, Medium, and Strong-each representing a distinct behavioral state observed across multiple feeding sessions. Images were extracted from 1080p video sequences recorded under varying illumination conditions and quality-checked to ensure visual clarity, color balance, and consistency. The resulting dataset provides a standardized benchmarking resource for evaluating computer vision and artificial intelligence models for automated recognition of feeding behavior and for optimizing intelligent feeding systems in aquaculture.</p>

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Tilapia Feeding Behavior Image Dataset: A Benchmark Resource for Automated Feeding Intensity Recognition in Aquaculture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Shahbaz Gul Hassan,
  • Yin Hang,
  • Murtaza Hasan,
  • Ferdous Sohel

摘要

Multiparametric video-based aquaculture monitoring systems are becoming essential for observing and quantifying fish behavior in real time. These platforms enable high-frequency, continuous recording of underwater activity, supporting automation in feeding management and behavioral analytics. The Tilapia Feeding Behavior Image Dataset (TFBID-mini) was generated using controlled recirculating aquaculture tanks equipped with high-definition underwater cameras to capture feeding events of Oreochromis niloticus (Nile tilapia). The dataset consists of 4,000 expert-annotated images classified into four feeding-intensity levels-None, Weak, Medium, and Strong-each representing a distinct behavioral state observed across multiple feeding sessions. Images were extracted from 1080p video sequences recorded under varying illumination conditions and quality-checked to ensure visual clarity, color balance, and consistency. The resulting dataset provides a standardized benchmarking resource for evaluating computer vision and artificial intelligence models for automated recognition of feeding behavior and for optimizing intelligent feeding systems in aquaculture.