Development of Behavior Monitoring System for Honeybees in Hive Using RFID sensors and Image Processing
Development of Behavior Monitoring System for Honeybees in Hive Using RFID sensors and Image Processing
复制标题
使用 RFID 传感器和图像处理开发蜂巢中蜜蜂行为监测系统
DOI：
10.1109/jcsse.2019.8864160
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Ai Hiroyuki
中科院分区：
文献类型：
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作者：
Takahashi Shinya;Hashimoto Koji;Maeda Sakashi;Li Yujie;Tsuruta Naoyuki;Ai Hiroyuki
最近，一个新的研究领域“计算行为学”不仅受到生物学家的关注，而且受到计算机科学家的关注，因为计算机技术的进步使动物行为的测量和分析成为可能。特别是分析工蜂在蜂巢内的交流行为，揭示蜜蜂的语言机制是行为学研究领域最重要和最有趣的问题之一。然而，这些分析通常是通过人工从记录的长时间视频数据中提取蜜蜂的行走轨迹来进行的。为了对蜜蜂的通信进行系统的理论分析，我们开发了一种基于图像处理的多只蜜蜂自动跟踪算法，并利用多台小尺寸单板计算机树莓派（Raspberry Pi），利用射频识别（RFID）传感器和高分辨率相机模块构建了一个长期跟踪蜜蜂行为的自动记录系统。利用该系统，我们从2015年到2018年，每年1 - 2次，在一个月的时间里，从早上6:30到晚上7:30进行记录实验。本文首先介绍了蜂箱中蜜蜂行为监测系统的概况。接下来，我们解释了我们提出的同步跟踪算法。最后给出了实验结果，验证了系统的性能。
Recently, a new research field “Computational Ethology” is attracting much attention from not only biologists but also computer scientists because advances in computer technology enabled to automate the measurement and the analysis of animal behavior. Especially, analyzing communications performed by honeybee workers in their hive is one of the most important and interesting issue in ethological research area to reveal a mechanism of honeybee's language. However, these analyses have been usually conducted by manually extracting honeybee's walking trajectories from recorded long-time video data. For a systematic and theoretical analysis of honeybee's communication, we have developed an automatic tracking algorithm of multiple honeybees using image processing and constructed an automatic recording system for long-term tracking of honeybee behaviors with Radio Frequency Identification (RFID) sensors and high-resolution camera modules using multiple small-size single board computers, Raspberry Pi. Using this system, we conducted recording experiments from 6:30 am to 7:30 pm during one month once or twice per a year from 2015 to 2018. In this paper, first we show the overview of a behavior monitoring system of honeybee in hive. Next, we explain the simultaneous tracking algorithm we proposed. Finally, we show the experimental results and confirm the system capabilities.