ミツバチコロニーの巣内行動観察システムの開発:RFIDセンサと画像処理を併用したコミュニケーション行動の自動検出  [in Japanese] Development of Behavior Monitoring System for Honeybees in Hive:Automatic Detection of Honeybee's Communication BehaviorsUsing RFID Sensors and Image Processing  [in Japanese]

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Author(s)

Abstract

<p>Nearly a hundred years ago, Karl von Frisch discovered that honeybees (Apis mellifera) communicate the exact location of food sources to other bees through a complex movement called waggle dance. Since then, analyzing communications performed by honeybee workers in their hive is one of the most important and interesting issues to reveal a mechanism of honeybee's language. However, it is not clear yet that the behavioral developmental process of young adult honeybees after their emerging adulthood. Our research focus is to analyze how they learn to dance well. These analyses have been usually conducted by extracting honeybee 's walking trajectories from recorded long-time video data manually. 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, RaspberryPi. The tiny RFID-tags are mounted on the dorsal tergum of young adult honeybees just after their emergence and two RFID antennas are arranged about 20 centimeters apart to determine the time whether each honeybee was entering or leaving the hive. Using this system, we conducted a recording experiment from 6:30 am to 7:30 pm over 4 weeks in September 2015. The size of our target colony is about 400 honeybees including a queen. The number of tagged honeybees is 100 individuals and the printed numbers are attached for each to be identified by observers. In this paper, first we show the background of our research and clarify requirements for a monitoring system of honeybee behaviors. Next, we show an overview of our system and explain the simultaneous tracking algorithm we proposed. Finally, we show the experimental results and discuss the system capabilities and some open problems.</p>

Journal

  • Transactions of the Japanese Society for Artificial Intelligence

    Transactions of the Japanese Society for Artificial Intelligence 32(4), B-GC2_1-11, 2017

    The Japanese Society for Artificial Intelligence

Codes

  • NII Article ID (NAID)
    130006887097
  • Text Lang
    JPN
  • ISSN
    1346-0714
  • Data Source
    J-STAGE 
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