Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
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DOI：
10.1109/tgrs.2018.2866056
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发表时间：
2019-03-01
影响因子：
8.2
通讯作者：
Guo, Qinghua
中科院分区：
文献类型：
--
作者：
Jin, Shichao;Su, Yanjun;Guo, Qinghua
作物表型性状的准确、高通量提取是分子育种的关键环节，对提高作物产量具有重要意义。然而，作为许多表型性状精确提取的前提条件，茎叶的自动分割仍然是一个很大的挑战。目前的研究主要集中在基于二维图像的分割，这是敏感的光照和遮挡。光探测与测距（LiDAR）以其主动激光扫描和强大的穿透能力，可以获得精确的三维信息，突破了表型从二维到三维的界限。然而，很少有研究解决的问题，基于激光雷达的茎叶分割。本文提出了一种中值归一化矢量生长（MNVG）算法，该算法分四步实现茎和叶的分割，预处理、茎生长、叶生长和后处理。用30个不同高度、紧密度、叶片数和密度的玉米样品对MNVG方法进行了试验。此外，使用真正分割的实例提取了叶、茎和个体水平的表型性状。在召回率、精确度、F分数和总体准确度方面，点水平分割的平均准确度分别为0.92、0.93、0.92和0.93。叶、茎和个体水平的表型性状提取精度分别为0.81 ~ 0.95、0.64 ~ 0.97和0.96 ~ 1。据我们所知，本文提出了第一个基于激光雷达的茎叶分割和表型性状提取方法在农业领域，这可能有助于基于激光雷达的植物音位学和精准农业的研究。
Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture.