• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

低成本蜜蜂监测电子标记系统。

Low-Cost Electronic Tagging System for Bee Monitoring.

机构信息

Data61|CSIRO, Sandy Bay, TAS 7005, Australia.

School of Technology, Environments and Design (TED), University of Tasmania, Sandy Bay, TAS 7005, Australia.

出版信息

Sensors (Basel). 2018 Jul 2;18(7):2124. doi: 10.3390/s18072124.

DOI:10.3390/s18072124
PMID:30004457
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6068632/
Abstract

This paper introduces both a hardware and a software system designed to allow low-cost electronic monitoring of social insects using RFID tags. Data formats for individual insect identification and their associated experiment are proposed to facilitate data sharing from experiments conducted with this system. The antennas' configuration and their duty cycle ensure a high degree of detection rates. Other advantages and limitations of this system are discussed in detail in the paper.

摘要

本文介绍了一种硬件和软件系统,旨在使用 RFID 标签实现低成本的社交昆虫电子监测。为了方便从使用该系统进行的实验中共享数据,提出了用于个体昆虫识别及其相关实验的数据格式。天线的配置及其占空比确保了高检测率。本文详细讨论了该系统的其他优点和局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/7b78ab2bd70b/sensors-18-02124-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/932e18f23bd7/sensors-18-02124-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/ffbe407244fa/sensors-18-02124-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/a510997aba09/sensors-18-02124-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/29b723f3c471/sensors-18-02124-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/d325175493d7/sensors-18-02124-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/96e6a7ff0342/sensors-18-02124-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/05b3e97afb20/sensors-18-02124-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/02f7b43793a0/sensors-18-02124-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/20c9aa4b615e/sensors-18-02124-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/8c07b85a5a7b/sensors-18-02124-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/b952c2a1a024/sensors-18-02124-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/7b78ab2bd70b/sensors-18-02124-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/932e18f23bd7/sensors-18-02124-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/ffbe407244fa/sensors-18-02124-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/a510997aba09/sensors-18-02124-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/29b723f3c471/sensors-18-02124-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/d325175493d7/sensors-18-02124-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/96e6a7ff0342/sensors-18-02124-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/05b3e97afb20/sensors-18-02124-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/02f7b43793a0/sensors-18-02124-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/20c9aa4b615e/sensors-18-02124-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/8c07b85a5a7b/sensors-18-02124-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/b952c2a1a024/sensors-18-02124-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f12/6068632/7b78ab2bd70b/sensors-18-02124-g012.jpg

相似文献

1
Low-Cost Electronic Tagging System for Bee Monitoring.低成本蜜蜂监测电子标记系统。
Sensors (Basel). 2018 Jul 2;18(7):2124. doi: 10.3390/s18072124.
2
Monitoring the use of anatomical teaching material using a low-cost radio frequency identification system: A comprehensive assessment.使用低成本射频识别系统监测解剖学教材的使用情况:一项综合评估。
Anat Sci Educ. 2016 Mar-Apr;9(2):197-202. doi: 10.1002/ase.1575. Epub 2015 Oct 6.
3
Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.基于树莓派的具有成本效益的小鼠射频识别标签,适用于自动化体内成像。
J Neurosci Methods. 2017 Jan 30;276:79-83. doi: 10.1016/j.jneumeth.2016.11.011. Epub 2016 Nov 27.
4
Challenges and prospects in the telemetry of insects.昆虫遥测技术的挑战与展望。
Biol Rev Camb Philos Soc. 2014 Aug;89(3):511-30. doi: 10.1111/brv.12065. Epub 2013 Oct 8.
5
Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (RFID).利用射频识别(RFID)技术测量群体饲养的个体猪的饮水行为。
Animal. 2016 Sep;10(9):1557-66. doi: 10.1017/S1751731115000774. Epub 2015 May 11.
6
An ultra-high frequency radio frequency identification system for studying individual feeding and drinking behaviors of group-housed broilers.用于研究群体饲养肉鸡个体采食和饮水行为的超高频射频识别系统。
Animal. 2019 Sep;13(9):2060-2069. doi: 10.1017/S1751731118003440. Epub 2019 Jan 11.
7
Assessing the Activity of Individual Group-Housed Broilers Throughout Life using a Passive Radio Frequency Identification System-A Validation Study.使用被动射频识别系统评估整个生命周期中单个群体饲养肉鸡的活动 - 验证研究。
Sensors (Basel). 2020 Jun 27;20(13):3612. doi: 10.3390/s20133612.
8
A system for implanting laboratory mice with light-activated microtransponders.一种用于给实验小鼠植入光激活微型应答器的系统。
J Am Assoc Lab Anim Sci. 2010 Nov;49(6):826-31.
9
Overview of RFID technology and its applications in the food industry.RFID 技术概述及其在食品工业中的应用。
J Food Sci. 2009 Oct;74(8):R101-6. doi: 10.1111/j.1750-3841.2009.01323.x.
10
RFID Technology for Management and Tracking: e-Health Applications.RFID 技术在管理和跟踪方面的应用:电子医疗应用。
Sensors (Basel). 2018 Aug 13;18(8):2663. doi: 10.3390/s18082663.

引用本文的文献

1
A versatile recording device for the analysis of continuous daily external activity in colonies of highly eusocial bees.一种多功能记录设备,用于分析高度群居蜜蜂群体中连续日常的外部活动。
J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2024 Nov;210(6):885-900. doi: 10.1007/s00359-024-01709-2. Epub 2024 Jun 20.
2
Propagation Analysis of an RFID System in the UHF Band in the Honeycomb Frame of a Beehive.超高频频段下的射频识别(RFID)系统在蜂巢蜂窝结构中的传播分析
Sensors (Basel). 2024 May 23;24(11):3356. doi: 10.3390/s24113356.
3
Correlation Between Increased Homing Flight Duration and Altered Gene Expression in the Brain of Honey Bee Foragers After Acute Oral Exposure to Thiacloprid and Thiamethoxam.

本文引用的文献

1
RFID tracking of sublethal effects of two neonicotinoid insecticides on the foraging behavior of Apis mellifera.RFID 追踪两种新烟碱类杀虫剂对蜜蜂觅食行为的亚致死效应。
PLoS One. 2012;7(1):e30023. doi: 10.1371/journal.pone.0030023. Epub 2012 Jan 11.
2
Automatic life-long monitoring of individual insect behaviour now possible.现在可以对个体昆虫行为进行自动终身监测。
Zoology (Jena). 2003;106(3):169-71. doi: 10.1078/0944-2006-00113.
3
Methods for marking insects: current techniques and future prospects.标记昆虫的方法:当前技术与未来前景
急性经口暴露于噻虫啉和噻虫嗪后,蜜蜂觅食者归巢飞行时间增加与大脑基因表达改变之间的相关性
Front Insect Sci. 2021 Dec 10;1:765570. doi: 10.3389/finsc.2021.765570. eCollection 2021.
4
Estimating the effect of tracking tag weight on insect movement using video analysis: A case study with a flightless orthopteran.利用视频分析估计追踪标签重量对昆虫运动的影响:以一种不能飞行的直翅目昆虫为例的研究。
PLoS One. 2021 Jul 22;16(7):e0255117. doi: 10.1371/journal.pone.0255117. eCollection 2021.
5
Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina.阿根廷西北部交配季节蜜蜂雄蜂活动分析
Insects. 2021 Jun 21;12(6):566. doi: 10.3390/insects12060566.
6
Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques.利用目标检测技术对蜜蜂的瓦螨寄生螨进行视觉诊断。
Sensors (Basel). 2021 Apr 14;21(8):2764. doi: 10.3390/s21082764.
7
A Novel Charging Method for Underwater Batteryless Sensor Node Networks.一种用于水下无电池传感器节点网络的新型充电方法。
Sensors (Basel). 2021 Jan 14;21(2):557. doi: 10.3390/s21020557.
8
Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services.基于智能 RFID 标签和物联网服务的成本效益型温度可追溯性系统的实现。
Sensors (Basel). 2020 Feb 20;20(4):1163. doi: 10.3390/s20041163.
9
An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.使用递归人工神经网络和属性选择预测亚马逊无刺蜜蜂觅食活动
Sci Rep. 2020 Jan 8;10(1):9. doi: 10.1038/s41598-019-56352-8.
Annu Rev Entomol. 2001;46:511-43. doi: 10.1146/annurev.ento.46.1.511.