An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants.

Sheng Wu,Xinyu Guo,Yongjian Wang,Jianjun Du,Chuanyu Wang,Weiliang Wen,Boxiang Xiao

doi:10.3389/fpls.2019.00248

Abstract

Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.

Highlights

Plant phenomics has gained more attention as a promising intervention in recent years, because it still remains a bottleneck that limits genetic gain in breeding programs (Araus et al, 2018)
Maize plants of different cultivars and growth stages were selected to evaluate the performance of the algorithm
The matching results of extracted skeleton and the original point cloud demonstrate that the proposed algorithm has a good performance and adaptability, and the approach is feasible for different plant size and different plant type plants

Summary

Introduction

Plant phenomics has gained more attention as a promising intervention in recent years, because it still remains a bottleneck that limits genetic gain in breeding programs (Araus et al, 2018). Accurate and high throughput measurement of highdimensional plant morphology across plant development is the essential component of plant phenotyping (Houle et al, 2010). Traditional phenotyping technologies are usually time consuming, low throughput, and labor intensive, which is far behind the development of genomics, efforts have been made to improve phenotyping efficiency (Yang et al, 2013; Zhang et al, 2017). There is none uniform solution for all kinds of plants, because plant morphology are diverse for different species. Specialized plant phenotyping algorithms have to be developed for architecture determined plants

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Discussion

Conclusion