摘要
植物三维表型结构信息在生物育种和基因组研究中尤为重要。为了能够有效、快速、无损地实现对植物三维表型信息的提取,以玉米为例,提出一种从图像生成的三维点云提取玉米幼苗叶片尺度的三维表型结构信息的方法。首先利用运动恢复结构算法将手机获取的图像重建生成三维点云;然后结合ExGR指数、条件欧氏聚类算法从环境背景中自动提取玉米幼苗,进而采用区域增长算法分割叶片;最后计算玉米幼苗的株高、三维体积、叶片面积和叶片周长等三维表型结构信息,并分析表型信息随时间的动态变化。结果表明,与真实值相比,所提方法计算的株高、叶片面积和叶片周长的均方根误差(RMSE)分别为0.77 cm、1.62 cm^(2)和1.21 cm,平均绝对百分比误差(MAPE)分别为3.23%、8.27%和4.75%,且决定系数R^(2)均达0.98以上。所提方法可以有效地无损提取玉米幼苗三维表型结构信息,并可以拓展到对其他柱状结构植物的表型信息提取方面。
In biological breeding and genomic research,the three-dimensional phenotypic structure information of plants is especially crucial.To extract the three-dimensional phenotypic information of plants efficiently,quickly,and nondestructively,taking corn as an example,a method for extracting the three-dimensional phenotypic structure information of maize seedling at leaf-scale from a three-dimensional point cloud produced from an image is proposed in this study.First,using a motion recovery structure algorithm,the image obtained from a mobile phone is rebuilt to produce a three-dimensional point cloud and then integrated with the ExGR index and conditional Euclidean clustering algorithm to automatically extract the corn seedlings from the surrounding environment.We employ the regional growth algorithm to segment the leaves.Finally,the three-dimensional phenotypic structure information of corn seedlings,including height,three-dimensional volume,leaf area,and leaf perimeter,are computed,and the dynamic changes of phenotypic information over time are examined.The findings demonstrate that the method in this study compares with the real value;the root mean square error(RMSE) of plant height,leaf area,and leaf circumference is 0.77 cm,1.62 cm^(2),and 1.21 cm,respectively;the mean absolute percentage error(MAPE) is 3.23%,8.27%,and 4.75% respectively;and the determination coefficient R^(2) reaches above 0.98.The proposed method can efficiently and nondestructively extract the three-dimensional phenotypic structure information of corn seedlings and can be extended to the extraction of other columnar structure plant phenotypic information.
作者
李少辰
张爱武
张希珍
杨志强
李梦南
Li Shaochen;Zhang Aiwu;Zhang Xizhen;Yang Zhiqiang;Li Mengnan(College of Resource Environment and Tourism,Capital Normal University,Beijing 100048,China;Key Laboratory of 3D Information Acquisition and Application,Ministry of Education,Capital Normal University,Beijing 100048,China;Engineering Research Center of Spatial Information Technology,Ministry of Education,Capital Normal University,Beijing 100048,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第2期61-69,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(42071303)
科技基础资源调查项目(2019FY101304)。
关键词
三维点云
植物表型
可见光植被指数
叶片分割
动态监测
3D point cloud
plant phenotype
visible light vegetation index
leaf segmentation
dynamic monitoring
