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A Lightweight YOLO-PEGA-Based Method for Quantifying Fish Feeding Intensity.

Xinyu Ai1,2, Shengmao Zhang1, Shenglong Yang1

  • 1Key Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China.

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Summary
This summary is machine-generated.

This study introduces a novel AI model to monitor fish feeding behavior by analyzing water splashes. This technology optimizes feeding schedules, reducing waste and improving aquaculture sustainability.

Keywords:
YOLO11attention mechanismfeeding intensitylarge yellow croaker

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Area of Science:

  • Aquaculture technology
  • Computer vision in agriculture
  • Animal behavior analysis

Background:

  • Manual or fixed-schedule fish feeding in aquaculture often leads to overfeeding, causing feed waste and water pollution.
  • Fish feeding behaviors, like jumping and competition, create splashes that can indicate hunger levels.

Purpose of the Study:

  • To develop an automated system for monitoring fish feeding intensity using computer vision.
  • To improve the efficiency and environmental sustainability of aquaculture operations.

Main Methods:

  • Constructed a frame-level splash-annotated dataset for training.
  • Developed a modified YOLO11 model (YOLO11-PEGA) with enhanced small-splash recognition and efficient downsampling.
  • Incorporated EGMA and an ADown operator into the model architecture.

Main Results:

  • The YOLO11-PEGA model achieved high precision (0.86) and recall (0.80) on the validation set.
  • Achieved mAP@0.5 > 0.80 and mAP@0.5-0.95 > 0.30.
  • Reduced model parameter count by 72.3% compared to the baseline, indicating improved efficiency.

Conclusions:

  • The proposed YOLO11-PEGA model offers stable detection performance in complex environments.
  • Provides valuable data for optimizing feeding thresholds, timing, and amounts in aquaculture.
  • Supports more efficient and sustainable aquaculture practices through automated monitoring.