基于分水岭分割算法对燕麦籽粒识别中粘连区域的分割  

Segmentation of adhesive area in oat grain recognition by watershed algorithm

作　　者：燕红文[1] 崔清亮 Yan Hongwen;Cui Qingliang(College of Information and Engineering,Shanxi Agricultural University,Taigu,Shanxi 030801,China)

机构地区：[1]山西农业大学信息科学与工程学院

出　　处：《计算机时代》2020年第1期23-25,共3页Computer Era

基　　金：国家重点研发计划资助项目（2016YFD0701801）;山西农业大学信科院教研管项目（xky19206）项目

摘　　要：为了适应智能农业需要,从图像处理角度研究燕麦清选时的籽粒识别与统计时遇到的粘连区域的问题。样品采集于左权县,基于大津法预处理燕麦,且通过分水岭分割算法来分割图像中粘结区域,分别采用内部标记与外部标记对燕麦与背景进行标记,可消除过度分割,识别率最高达到98.55%,研究表明,该算法对于清选后粘连较少的图像处理效果好,可对燕麦清选损失率的在线监测提供理论和方法支持。In order to meet the needs of intelligent agriculture,this study studied the problem of adhesion areas encountered in grain identification and statistics during oat picking from the perspective of image processing.The samples were collected in Zuoquan County,the oats images were pretreated with the Otsu method,and the watershed algorithm was used to segment the adhesion areas in the images.The oats and the background were marked by internal markers and external markers,respectively.The over-segmentation can be eliminated,and the recognition rate is up to 98.55%.The result shows that the algorithm has good processing effect on the images with less adhesion after cleaning,and can provide theoretical and methodological support for online monitoring of oatmeal cleaning loss rate.

关 键 词：燕麦 分水岭分割 损失率 粘连 

分 类 号：S226.9[农业科学—农业机械化工程]