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Design and experiment of intelligent weed recognition system

机译：智能杂草识别系统的设计与实验

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为了减少农药的使用量,设计了一种新型杂草智能识别系统,并在不同的环境下进行了大量试验.该系统主要由智能识别控制器、喷头总成于安装支架组成,通过摄像头实时采集田间图像,采用基于颜色与位置特征的识别算法分析杂草分布情况,控制喷头快速开闭,实现精准对靶喷药.试验证明,该系统对不同环境均具有较好地适应性,能够快速、准确、可靠的定位杂草.在普通环境下系统识别准确率为97.0％;在强光照环境下系统识别准确率为92.5％；在阴影环境下系统识别准确率为89.2％,单帧图像平均耗时160 ms.该研究可为田间精确喷施除草装置的研发提供参考.%In order to reduce the application amount of pesticide,the intelligent weed recogrnition system was designed,which was composed of intelligent controller,nozzle assembly and installing bracket,and a new weed recognition method based on color and location characteristics was applied to the different environment in this paper.Experimental results showed that the intelligent weed recognition system was effective in the different environments,and could locate the weed rapidly,reliably and accurately.The recognition correct rate of the intelligent weed recognition system was 97.0％ in the ordinary environment,92.5％ in strong illumination environment,and 89.2％ in the shadow environment.The average recognition time was 160 ms.The research can provide a reference for design of the precise spraying weeding system based on machine vision.
机译：为了减少农药的使用量,设计了一种新型杂草智能识别系统,并在不同的环境下进行了大量试验.该系统主要由智能识别控制器、喷头总成于安装支架组成,通过摄像头实时采集田间图像,采用基于颜色与位置特征的识别算法分析杂草分布情况,控制喷头快速开闭,实现精准对靶喷药.试验证明,该系统对不同环境均具有较好地适应性,能够快速、准确、可靠的定位杂草.在普通环境下系统识别准确率为97.0％;在强光照环境下系统识别准确率为92.5％；在阴影环境下系统识别准确率为89.2％,单帧图像平均耗时160 ms.该研究可为田间精确喷施除草装置的研发提供参考.%In order to reduce the application amount of pesticide,the intelligent weed recogrnition system was designed,which was composed of intelligent controller,nozzle assembly and installing bracket,and a new weed recognition method based on color and location characteristics was applied to the different environment in this paper.Experimental results showed that the intelligent weed recognition system was effective in the different environments,and could locate the weed rapidly,reliably and accurately.The recognition correct rate of the intelligent weed recognition system was 97.0％ in the ordinary environment,92.5％ in strong illumination environment,and 89.2％ in the shadow environment.The average recognition time was 160 ms.The research can provide a reference for design of the precise spraying weeding system based on machine vision.

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