用传感器数据+机器学习，精准识别牛的日常行为和发情期。
Classification of Cattle Behavior and Detection of Heat (Estrus) using Sensor Data
- 用蓝牙颈圈采集加速度与陀螺仪数据，云端同步实时行为信息。
- 行为分类准确率超93%，发情期检测准确率达96%。
- 低成本方案适合资源有限的养殖场推广使用。
本文提出一种基于传感器数据与机器学习的牛只行为监测与发情期（heat）检测新系统。设计并部署了一款低成本蓝牙颈圈，集成加速度计与陀螺仪传感器，实时采集真实奶牛的行为数据，并同步至云端。通过同步的CCTV视频标注，构建了包含进食、反刍、躺卧等行为的标签数据集。评估了多种机器学习模型——支持向量机（SVM）、随机森林（RF）与卷积神经网络（CNN）在行为分类中的表现；此外，采用长短期记忆网络（LSTM）结合行为模式与异常检测进行发情期识别。在有限测试集上，系统实现超过93%的行为分类准确率与96%的发情期检测准确率。该方法为资源受限环境下的精准畜牧监控提供了可扩展且易获取的解决方案。
原文摘要 · Abstract (English)
This paper presents a novel system for monitoring cattle behavior and detecting estrus (heat) periods using sensor data and machine learning. We designed and deployed a low-cost Bluetooth-based neck collar equipped with accelerometer and gyroscope sensors to capture real-time behavioral data from real cows, which was synced to the cloud. A labeled dataset was created using synchronized CCTV footage to annotate behaviors such as feeding, rumination, lying, and others. We evaluated multiple machine learning models -- Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) -- for behavior classification. Additionally, we implemented a Long Short-Term Memory (LSTM) model for estrus detection using behavioral patterns and anomaly detection. Our system achieved over 93% behavior classification accuracy and 96% estrus detection accuracy on a limited test set. The approach offers a scalable and accessible solution for precision livestock monitoring, especially in resource-constrained environments.
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