Abstract 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. Similar content being viewed by others Funding This work is partially supported by the Multi-Source Perception and Intelligent Computing Laboratory of SZTU, the Natural Science Foundation of Top Talent of SZTU (GDRC202411, GDRC202412), the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) (GML-KF-24-05). Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from