生物学杂志

生物学杂志

• 研究报告 • 上一篇    下一篇

基于蜂群声音子带功率比的分蜂预测

  

  1. 1. 中国科学院合肥物质科学研究院 技术生物与农业工程研究所， 合肥 230031；2. 中国科学技术大学 研究生院科学岛分院， 合肥 230026
  • 出版日期:2018-10-18 发布日期:2018-10-18
  • 作者简介:吕竹青，硕士研究生，研究方向为生物物理学，E-mail：wyplzq@mail.ustc.edu.cn
  • 基金资助:
    国家自然基金（30870445）

The prediction of honeybee colony swarming based on the sub-band power ratio of colony sound

  1. 1. Institute of Technical Biology and Agriculture Engineering, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031; 2.Science Island Branch of Graduate School,University of Science and Technology of China, Hefei 230026, China
  • Online:2018-10-18 Published:2018-10-18

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摘要： 通过监测蜂群声音帮助蜂农识别蜂群健康状况、预测分蜂等是精准管理蜂群的一个重要手段。目前蜂群声音识别器一般基于蜂群声音多个特征的机器学习算法构建的。以中华蜜蜂(Apis ceranan)为研究对象，利用“人工分蜂”方法获取分蜂现象，分析了分蜂和正常蜂群声音信号的功率谱密度。结果表明，分蜂前和准备分蜂时的蜂群声音在频率分布上有明显差异，正常蜂群声音的最大功率密度位于0～200 Hz之间，准备分蜂时蜂群声音的最大功率密度位于200～400 Hz之间。以子带功率比为特征向量，基于CART决策树算法构建了蜂群声音分类识别器，该声音识别器预测分蜂的先验概率可达99.04%。为发展蜂群声音识别器提供了新的技术参数。

关键词: 中华蜜蜂；蜂群声音；分蜂, 子带功率比；机器学习；决策树

Abstract: Beehive sound-based monitoring the states of a honeybee colony, including health and preswarming, is an important means for precise beekeeping. However, the present classifiers of a beehive sound are mostly developed according to the multiple features of the beehive sound using complex algorithms of machine learning. In the study, the "artificial swarming" method to get the beehive swarming was used. The beehive sounds of Apis cerana were recorded and analyzed under preswarming and non-preswarming conditions, and the power spectral density and the sub-band power ratio of the sounds were extracted. The results showed that the frequency ranges of the non-preswarming beehive sound were mainly within 0-200 Hz, while those of the preswarming beehive sound within 200-400 Hz. Based on the sub-band power ratio of the beehive sound, a machine learning classifier was developed for predicting the preswarming state of a honeybee colony with a CART decision tree algorithm, and a higher prior probability of 99.04% was achieved. This study would provide a new feature for developing the classifiers of a beehive sound.

Key words: Apis cerana, beehive sound, swarming, sub-band power ratio, machine learning, decision tree