Computer Engineering Department, İzmir Institute of Technology, Izmir 35430, Türkiye.
Computer Engineering Department, Aydın Adnan Menderes University, Aydın 09100, Türkiye.
Sensors (Basel). 2023 Dec 13;23(24):9795. doi: 10.3390/s23249795.
Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming.
准确预测发情期对于优化动物养殖中的配种效率和降低成本至关重要，而动物养殖是全球粮食生产的重要环节。精确的发情期确定对于避免经济损失至关重要，例如牛奶产量减少、小牛出生延迟以及失去政府支持的资格等。
所提出的方法将发情期检测与使用增强现实（AR）的牛识别相结合。它首先启动基于深度学习的交配检测，然后使用 YOLOv5 识别感兴趣的交配区域（ROI）。然后使用 YOLOv5 对 ROI 进行裁剪，并在裁剪的 ROI 上执行牛 ID 检测。系统随后记录识别出的牛 ID。
所提出的系统以 99%的准确率准确检测交配行为，以 98%的准确率识别发生交配的 ROI，并以 94%的准确率检测交配对。该系统在所有操作中都取得了很高的成功率，这表明它在动物养殖中的 AR 和人工智能应用方面具有潜在的贡献。
