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Published on: August 31, 2018
BoviFusionNet: A Lightweight Edge-Deployable AI System for Cattle Behavior Recognition in Livestock Monitoring
Jiawen Li1,2,3, Weidong Zhang1, Ximing Ren1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Veterinary Sciences
|July 28, 2026
Summary
This study introduces BoviFusionNet, an AI system for real-time cattle behavior recognition on farms. It offers an accurate, lightweight solution for monitoring animal welfare and health without cloud reliance.
Area of Science:
- Agricultural Technology
- Artificial Intelligence
- Animal Behavior Analysis
Background:
- Automated monitoring of cattle behaviors (eating, standing, lying) is crucial for animal welfare and health assessment.
- Farm deployment faces challenges like data imbalance, dense groupings, scale variation, occlusion, and edge computing limitations.
Purpose of the Study:
- Develop a lightweight, edge-deployable AI system for real-time, non-contact cattle behavior recognition.
- Address practical farm deployment challenges to enable continuous, non-invasive monitoring.
Main Methods:
- Proposed BoviFusionNet, a lightweight AI model built upon YOLO11n.
- Integrated information-preserving downsampling (ADown), adaptive bidirectional feature fusion (BiFPN), and local window attention (C2CGA).
- Employed a box balanced augmentation strategy for rebalancing training data.
Main Results:
- BoviFusionNet achieved 0.7851 recall, 0.7763 F1-score, and 0.7976 mAP@0.50.
- Demonstrated a 9.92% improvement in mAP@0.50:0.95 compared to the baseline YOLO11n.
- Achieved real-time inference at 28.08 FPS on an edge device with a 3.4 MB model size and 5.4 GFLOPs.
Conclusions:
- BoviFusionNet provides an effective accuracy-complexity trade-off for on-farm edge AI applications.
- Enables continuous, non-invasive monitoring of health-relevant cattle behaviors.
- Offers fundamental behavioral data for veterinary health assessment tools without cloud or wearable sensors.
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