Automatic analysis of camera image data: an example of honey bee (Apis cerana) images from the Shanping wireless sensor network.

Q4 Agricultural and Biological Sciences Taiwan Journal of Forest Science Pub Date : 2011-09-01 DOI:10.7075/TJFS.201109.0305
Lu Sheng-shan, M. Perry, Michael Nekrasov, T. Fountain, P. Arzberger, Wang Yuhuang, Lin ChauChin
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Abstract

Under an international collaborative program between the Taiwan Forestry Research Institute (TFRI) and Pacific RIM undergraduate experience (PRIME) of San Diego University, San Diego, CA, USA in 2010, we extended an image analysis package and applied it to honey bee observations. In this article, we describe the results of this collaboration. A tool suitable for routine measurements and counting tasks was developed to perform an automatic process. We applied blob-detecting of a computer vision technique to develop this package. We then tested the tool using images with different numbers of bees present collected from the Shanping wireless sensor network of TFRI. We compared the times consumed between the automatic and manual processes. Results showed that analysis of images with a low number of bees present (with an average bee number of <30 individuals per image) between the automatic process and manual process respectively required 9 and 315 min. A similar results showed that analysis of images with a high number of bees present (with an average bee number of >30 individuals per image) between the automatic process and manual process respectively require 23 and 409 min. Although the automatic process overestimated bee counts by 2~21%, the tool shows significant reductions in processing times. We concluded that the program provides a convenient way to determine the target and thus facilitate the examination of a large volume of honey bee images from a wireless sensor network in the field.
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相机图像数据的自动分析:以汕坪无线传感器网络中的蜜蜂图像为例。
2010年,在台湾林业研究所(TFRI)与美国圣地亚哥大学(San Diego, CA, USA)的太平洋环太平洋本科生经验(PRIME)的国际合作项目中,我们扩展了图像分析包并将其应用于蜜蜂观测。在本文中,我们将描述这种协作的结果。开发了一种适用于日常测量和计数任务的工具,以执行自动过程。我们应用计算机视觉的斑点检测技术来开发这个包。然后,我们使用从TFRI山坪无线传感器网络收集的不同数量蜜蜂的图像对该工具进行了测试。我们比较了自动过程和手动过程所消耗的时间。结果表明,分析图像的低数量的蜜蜂礼物(平均蜜蜂数量的< 30个人/图片)之间的自动流程和手动流程分别需要9和315分钟。类似的结果表明,图像分析与大量的蜜蜂礼物(平均蜜蜂数量> 30个人/图片)之间的自动流程和手动流程分别需要23和409分钟。尽管自动过程高估了蜜蜂该工具的加工时间显著缩短了2~21%。我们得出的结论是,该程序提供了一种方便的方法来确定目标,从而促进了现场无线传感器网络中大量蜜蜂图像的检查。
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来源期刊
Taiwan Journal of Forest Science
Taiwan Journal of Forest Science Agricultural and Biological Sciences-Forestry
CiteScore
0.20
自引率
0.00%
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0
期刊介绍: The Taiwan Journal of Forest Science is an academic publication that welcomes contributions from around the world. The journal covers all aspects of forest research, both basic and applied, including Forest Biology and Ecology (tree breeding, silviculture, soils, etc.), Forest Management (watershed management, forest pests and diseases, forest fire, wildlife, recreation, etc.), Biotechnology, and Wood Science. Manuscripts acceptable to the journal include (1) research papers, (2) research notes, (3) review articles, and (4) monographs. A research note differs from a research paper in its scope which is less-comprehensive, yet it contains important information. In other words, a research note offers an innovative perspective or new discovery which is worthy of early disclosure.
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