Title
Stem–Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data
Abstract
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.
Year
DOI
Venue
2019
10.1109/TGRS.2018.2866056
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Laser radar,Agriculture,Robustness,Image segmentation,Distance measurement,Genomics,Bioinformatics
Computer vision,Laser scanning,Pattern recognition,Segmentation,Image segmentation,Lidar,Preprocessor,Artificial intelligence,Molecular breeding,Mathematics,Phenotypic trait,Stem-and-leaf display
Journal
Volume
Issue
ISSN
57
3
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Shichao Jin112.06
Yanjun Su202.03
Fangfang Wu3469.56
Shuxin Pang401.01
Shang Gao529159.33
Tianyu Hu603.38
Jin Liu7111.92
Q. H. Guo8253.61