摘要
对玉米植株图像的茎叶进行分割,为组分表型分析、植株对环境胁迫的响应等后续研究提供参数和依据。采用内布拉斯加大学林肯分校植物表型数据集中的玉米植株图像,自动进行玉米植株图像的裁剪和二值化;通过骨架细化算法建立植株的骨架模型,并设定阈值去除骨架中的毛刺;然后检测骨架上的端点和交叉点,采用二叉树分析骨架模型,最终确立各级节点及各个叶片,从而实现茎叶分割。结果表明,建立的玉米植株茎叶分割算法,具有处理速度快、易于理解等优点,为玉米等植物表型分析与育种等提供支持。
Segmentation of stems and leaves of corn plant images could provide parameters and basis for subsequent studies such as phenotypic analysis and plant response to environmental stress.Maize plant images were selected from the plant phenotypic dataset of the University of Nebraska-Lincoln,and the maize plants image were tailored and binarized automatically.The skeleton model of the plant was established by the skeleton refinement algorithm,and the threshold was set to remove the burrs in the skeleton;then the end points and branch points on the skeleton were detected,the skeleton model was analyzed by the binary tree,and the nodes at each level and the individual leaves were finally established,thereby realized the segmentation of the stems and leaves.The results showed that the established stem and leaf segmentation algorithm of maize plants has the advantages of fast processing speed and easy understanding,and provides support for the phenotypic analysis and breeding of plants such as maize.
作者
张卫正
李旭光
万瀚文
李灿林
张伟伟
金保华
刘岩
ZHANG Weizheng;LI Xuguang;WAN Hanwen;LI Canlin;ZHANG Weiwei;JIN Baohua;LIU Yan(School of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;International College of Zhengzhou University,Zhengzhou 450000,China)
出处
《河南农业科学》
北大核心
2020年第9期166-172,共7页
Journal of Henan Agricultural Sciences
基金
国家自然科学基金项目(61403349,41601418,6160031133)
河南省科技攻关项目(182102110399,192102110203)
河南省高等学校重点科研项目(18A210025,20A520004)。
关键词
玉米植株
植物表型
图像处理
骨架提取
二叉树分析
茎叶分割
Maize plant
Plant phenotype
Image processing
Skeleton extraction
Binary tree analysis
Stem and leaf segmentation
