基于骨架和最优传输距离的玉米点云茎叶分割和表型提取

Segmentation and phenotypic trait extraction of maize point cloud stem-leaf based on skeleton and optimal transportation distances

针对当前三维点云分割方法难以精确分割玉米植株顶部新叶的问题,该研究提出一种基于点云骨架和最优传输距离的玉米点云茎叶分割方法。首先利用拉普拉斯骨架提取算法获得植株骨架;其次根据玉米形态结构特征将植株骨架分解成器官子骨架,并实现器官粗分割;再以最优传输距离作为点云距离度量,采用从上到下的顺序对未分割点云进行精细分割;最后自动提取株高、冠幅、茎高、茎粗、叶长和叶宽6种表型参数。研究结果表明,茎叶分割的平均精确度、平均召回率、平均微F1分数和平均总体准确率分别为0.967、0.961、0.964和0.967;6个表型参数的提取值与实测值具有较强的相关性,决定系数分别为0.99、0.99、0.96、0.97、0.93和0.96。该研究方法能对茎叶器官进行精确分割,为玉米高通量表型检测、三维几何重建等提供了一种有效技术手段。

Accurate and high-throughput maize plant phenotyping is vital for crop breeding and cultivation research.However,fully automatic and fine stem-leaf segmentation of maize shoots from three-dimensional point clouds is still challenging,especially for the newly emerging leaves that are close to each other during the seedling stage.To address this issue,this study proposed an automatic segmentation method consisting of six main steps,including skeleton extraction,skeleton decomposition,point cloud coordinate system transformation,coarse segmentation,fine segmentation,and phenotypic parameter extraction.The Laplacian-based skeleton extraction algorithm was used to extract the maize plant skeleton in the skeleton extraction step.In the process of skeleton decomposition,the plant skeleton was decomposed into a stem sub-skeleton and some leaf sub-skeletons based on the morphological characteristics of leaves.A plant spatial coordinate system was constructed.The Z-axis of this coordinate system coincided with the stem axis,which could be better to represent the morphology of maize plants.The plant point cloud and skeleton vertices were transformed from the original coordinate system to the plant spatial coordinate system,and the subsequent point cloud segmentation steps were carried out in the plant spatial coordinate system.In the coarse segmentation step,using the decomposed organ sub-skeletons,the plant point cloud was roughly segmented into stem and leaf instances.However,the stem instance contained some wrongly segmented points,and these points should belong to the leaf instances.In the fine segmentation step,firstly,the wrong points were identified and removed from the stem instance,and then the stem-leaf classification algorithm based on the optimal transportation distance was used to segment these points into the correct organ instance.The classification algorithm used top-down order to classify points based on optimal transportation distances,which was equivalent to adopting a segmentation strategy from the leaf tip to the stem.The top-down order was critical as it facilitated the complete utilization of the segmented point cloud information in the new leaf while classifying the points.At the same time,it also ensured that the points belonging to the new leaves were determined first.Compared with the Euclidean distance,the optimal transportation distances described the local geometric characteristics of the point cloud more accurately,which helped to deal with the case of new leaves wrapping each other.Based on the segmented organ instances,six phenotypic parameters were accurately and automatically measured,including plant height,crown diameter,stem height and diameter,leaf width,and length.The segmentation method was tested on 30 maize plants and compared with manually obtained ground truth.Average precision,average recall rates,average micro F1-scores,and average overall accuracy of the stem-leaf segmentation algorithm were 0.967,0.961,0.964,and 0.967,respectively.To compare the phenotypic parameters obtained by the proposed method in this study and those obtained by manual measurement,the regression analysis was done with the root mean square error.Results showed that the determination coefficients of plant height,crown diameter,stem height,stem diameter,leaf width and leaf length were 0.99,0.99,0.96,0.97,0.93,and 0.96,respectively;the root mean square errors were 1.71,3.44,7.07,0.41,0.85,and 5.28 cm,respectively.The results indicated that the proposed algorithm could automatically and precisely segment not only the fully expanded leaves but also the new leaves wrapped together and closed together.The proposed approach might play an important role in further maize research and applications,such as genotype-to-phenotype study and geometric reconstruction.