Accurate estrus detection is essential for dairy farm productivity, yet traditional manual and wearable methods remain limited by high labor costs, delayed responses, and animal stress. To address these challenges, we propose CE-YOLO, a lightweight YOLOv11n-based vision model tailored for edge deployment, which detects mounting behavior by integrating a Channel-Aware Downsampling (CA-Down) module to preserve small-scale features, a SimSPPF module for efficient contextual fusion, and a DySample module for dynamic spatial alignment. Experiments on a curated estrus behavior dataset demonstrate that CE-YOLO achieves a precision of 94.9% and an mAP50 of 98.2%, significantly outperforming the baseline by 3.9% and 4.6% respectively. These results validate the model as an efficient, non-intrusive solution for real-time estrus monitoring, strongly supporting the advancement of smart livestock management.
Zhao et al. (2026) studied this question.