School of Computer Science and Engineering, Bangor University, Bangor LL57 2DG, UK.
School of Natural Sciences, Bangor University, Bangor LL57 2DG, UK.
Sensors (Basel). 2023 Jun 1;23(11):5250. doi: 10.3390/s23115250.
Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.
详细内容包括尝试实现一个实时雷达信号分类系统，以监测和计算蜂巢入口处的蜜蜂活动。人们对记录蜜蜂的生产力很感兴趣。入口处的活动可以很好地衡量整体健康状况和能力，而基于雷达的方法可能比其他技术更便宜、低功耗且多功能。全自动系统可以从多个蜂巢同时大规模捕获蜜蜂活动模式，为生态研究和业务实践改进提供重要数据。从农场的管理蜂箱中收集了多普勒雷达的数据。记录被分成 0.4 秒的窗口，并从数据中计算出对数区域比（LAR）。使用摄像机记录的视觉确认，训练支持向量机模型来识别 LAR 中的飞行行为。还使用相同的数据研究了语谱图深度学习。完成后，该过程将允许移除摄像机并仅通过基于雷达的机器学习准确计数事件。来自更复杂蜜蜂飞行的挑战性信号阻碍了进展。系统的准确率达到了 70%，但是杂乱信号会影响整体结果，需要智能滤波来从数据中去除环境影响。
