Tilapia Feeding Behavior Image Dataset: A Benchmark Resource for Automated Feeding Intensity Recognition in Aquaculture
Scientific data, In Press
2026
: 42362584
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.
- Tilapia Feeding Behavior Image Dataset: A Benchmark Resource for Automated Feeding Intensity Recognition in Aquaculture
- Shahbaz Gul Hassan - Zhongkai University of Agriculture and EngineeringYin Hang - Shenzhen Technology UniversityMurtaza Hasan - Islamia University of BahawalpurFerdous Sohel - Murdoch University
- Scientific data, In Press
- GDRC202411, GDRC202412 / Natural Science Foundation of Top Talent of SZTU
- 991005896971207891
- © The Author(s) 2026
- School of Information Technology
- English
- Journal article
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