首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Development of an Optoelectronic Sensor for Detecting and Classifying Fruit Fly (Diptera: Tephritidae) for Use in Real-Time Intelligent Traps
【2h】

Development of an Optoelectronic Sensor for Detecting and Classifying Fruit Fly (Diptera: Tephritidae) for Use in Real-Time Intelligent Traps

机译:用于实时智能陷阱的果蝇(双翅目:蝇科)的检测和分类光电传感器的开发

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Fruit flies (Diptera: Tephritidae) cause losses to world fruit growing. For a fast and effective control of the pest, it is necessary to identify the species and their populations. Thus, we developed an infrared optoelectronic sensor using phototransistors to capture the signal of the partial occlusion of the infrared light caused by the beating of the fly wings. Laboratory experiments were conducted using the sensor to capture the wing beat signal of A. fraterculus and C. capitata. The captured signals were used to obtain the characteristics of the flies’ wing beats frequency and for a production of a dataset made available as one of the results of this work. For the passage detection, we developed the algorithm of detection of events of passage (PEDA) that uses the root mean square (RMS) value of a sliding window applied to the signal compared to a threshold value. We developed the algorithm of detection of events of passage (CAEC) that uses the techniques of autocorrelation and Fourier transform for the extraction of the characteristics of the wings’ beat signal. The results demonstrate that it is possible to use the sensor for the development of an intelligent trap with detection and classification in real time for A. fraterculus and C. capitata using the wing beat frequency obtained by the developed sensor.
机译:果蝇(双翅目:蝇科)对世界水果种植造成损失。为了快速有效地防治害虫,有必要确定物种及其种群。因此,我们开发了一种使用光电晶体管的红外光电传感器,以捕获由飞翼的跳动引起的红外光部分遮挡的信号。使用该传感器进行了实验室实验,以捕获fraterculus和C. capitata的翅膀节拍信号。捕捉到的信号用于获得果蝇拍子频率的特性,并用于生成数据集,作为这项工作的结果之一。对于通过检测,我们开发了一种检测通过事件的算法(PEDA),该算法使用应用于信号的滑动窗口的均方根(RMS)值与阈值进行比较。我们开发了通过事件检测算法(CAEC),该算法使用自相关和傅里叶变换技术提取机翼拍信号的特征。结果表明,可以使用该传感器开发智能陷阱,并利用通过开发的传感器获得的机翼拍频对实时荧光粉虱和角头梭菌进行实时检测和分类。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号