基于图像的植物群体冠层结构动态定量和高精度 3D 评估。
Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations.
机构信息
Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Institute of Vegetables and Flowers, Chinese Academy of Agricultural Science, Beijing, China.
出版信息
Ann Bot. 2018 Apr 18;121(5):1079-1088. doi: 10.1093/aob/mcy016.
BACKGROUND AND AIMS
Global agriculture is facing the challenge of a phenotyping bottleneck due to large-scale screening/breeding experiments with improved breeds. Phenotypic analysis with high-throughput, high-accuracy and low-cost technologies has therefore become urgent. Recent advances in image-based 3D reconstruction offer the opportunity of high-throughput phenotyping. The main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach, and to monitor plant growth and development from seedling stage to fruiting stage.
METHODS
Multi-view images of flat-leaf cucumber, small-leaf pepper and curly-leaf eggplant were obtained by moving a camera around the plant canopy. Three-dimensional point clouds were reconstructed from images based on the MVS approach and were then converted into surfaces with triangular facets. Phenotypic parameters, including leaf length, leaf width, leaf area, plant height and maximum canopy width, were calculated from reconstructed surfaces. Accurate evaluation in 2D and 3D for individual leaves was performed by comparing reconstructed phenotypic parameters with referenced values and by calculating the Hausdorff distance, i.e. the mean distance between two surfaces.
KEY RESULTS
Our analysis demonstrates that there were good agreements in leaf parameters between referenced and estimated values. A high level of overlap was also found between surfaces of image-based reconstructions and laser scanning. Accuracy of 3D reconstruction of curly-leaf plants was relatively lower than that of flat-leaf plants. Plant height of three plants and maximum canopy width of cucumber and pepper showed an increasing trend during the 70 d after transplanting. Maximum canopy width of eggplants reached its peak at the 40th day after transplanting. The larger leaf phenotypic parameters of cucumber were mostly found at the middle-upper leaf position.
CONCLUSIONS
High-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.
背景与目的
全球农业正面临着一个表型瓶颈的挑战，这是由于大规模的筛选/培育实验带来了改良品种。因此，高通量、高精度和低成本的技术进行表型分析已变得尤为迫切。基于多视角立体（MVS）方法的图像三维重建技术的最新进展为高通量表型分析提供了机会。本研究的主要目的是基于多视角立体方法定量和评估二维和三维植物群体的冠层结构，并从幼苗期到结果期监测植物的生长和发育。
方法
通过移动相机环绕植物冠层，获取了扁叶黄瓜、小果辣椒和卷叶茄子的多视角图像。基于多视角立体方法，从图像中重建三维点云，并将其转换为具有三角形面的表面。从重建的表面计算出叶片长度、叶片宽度、叶面积、植株高度和最大冠层宽度等表型参数。通过将重建的表型参数与参考值进行比较，并计算 Hausdorff 距离（即两个表面之间的平均距离），对个体叶片在 2D 和 3D 中的精确评估。
结果
我们的分析表明，参考值和估计值之间的叶片参数具有良好的一致性。基于图像的重建表面和激光扫描之间也发现了高度的重叠。卷叶植物的 3D 重建精度相对较低。三种植物的株高和黄瓜、辣椒的最大冠层宽度在移栽后 70 天内呈增加趋势。茄子的最大冠层宽度在移栽后第 40 天达到峰值。黄瓜较大的叶片表型参数主要出现在中上部叶片位置。
结论
高质量的 3D 重建评估表明，基于 MVS 方法的 3D 冠层动态捕捉可应用于 3D 表型分析，用于选育和田间管理等应用。
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