Efficient recognition of fish feeding behavior: A novel two-stage framework pioneering intelligent aquaculture strategies

• Published in: Computers and electronics in agriculture Vol. 224; p. 109129
• Main Authors: Cai, Kewei, Yang, Zhipeng, Gao, Tianyi, Liang, Meng, Liu, Peiyang, Zhou, Siyi, Pang, Hongshuai, Liu, Ying
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2024
• Subjects: Action recognition; Aquaculture; Fish feeding behavior; Reparameterization; YOLOv8; Fish feeding behavior; Action recognition; Reparameterization; YOLOv8; Aquaculture
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.109129

In the domain of aquaculture, the act of feeding fish is a pivotal factor influencing both the growth of the fish and the associated cultivation costs. The implementation of intelligent feeding strategies is a crucial prerequisite for maintaining fish health and minimizing costs, with the accurate discernment of fish feeding behavior serving as the fundamental basis for the realization of such strategies. Addressing the issues of high data redundancy and substantial noise content inherent in the datasets utilized by existing identification models, as well as the intricate design and suboptimal execution efficiency of the model structures, this study introduces a two-stage framework for discriminating fish feeding behavior. In the initial stage, a re-parameterizable multi-scale object detection model is established, facilitating the acquisition of the spatial distribution of fish schools. This process effectively eliminates redundant data and noise, decouples the training and inference processes of the model, equivalently transforms the complex network training weights, and simplifies the inference process of the model. An analysis of the dataset characteristics is conducted, leading to the optimization of the model detector’s design and a reduction in both the model parameters and computational requirements. In the subsequent stage, responding to the key data characteristics of the spatial distribution of fish schools, a lightweight behavior recognition model is designed. This model enables the rapid and accurate identification of fish feeding behavior. A plethora of experimental results demonstrate that the proposed method can achieve a high recognition accuracy (Acc 83.33%) while operating under the constraints of minimal model parameters and computations (6.45M Params, 8.135G FLOPs). This provides a robust model foundation for the industrial application of the algorithm, underscoring the significant potential of the proposed method in advancing intelligent feeding strategies in aquaculture.
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•A comprehensive dual-phase analytical framework is introduced for examining feeding patterns.•An innovative method integrating multi-scale information fusion and lightweight reparameterization backbone is proposed.•A classification model is presented to capture temporal and spatial complexities in fish feeding behaviors.•Achieved 83.33% accuracy, providing compelling evidence of its proficiency in analyzing fish feeding behavior.