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Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level

Shichao Jin, Yanjun Su, Shilin Song, Kexin Xu, Tianyu Hu, Qiuli Yang, Fangfang Wu, Guangcai Xu, Qin Ma, Hongcan Guan, Shuxin Pang, Yumei Li (+1 others)
2020 Plant Methods  
In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e  ...  The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize  ...  maize segmentation, phenotypic trait extraction, and biomass estimation at individual plant level. c Stem-leaf segmentation from individual plant, phenotypic trait extraction, and biomass estimation at  ... 
doi:10.1186/s13007-020-00613-5 pmid:32435271 pmcid:PMC7222476 fatcat:bwwftyv2zndxrp4jtpb77ybe2q

Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms

Shichao Jin, Yanjun Su, Shang Gao, Fangfang Wu, Tianyu Hu, Jin Liu, Wenkai Li, Dingchang Wang, Shaojiang Chen, Yuanxi Jiang, Shuxin Pang, Qinghua Guo
2018 Frontiers in Plant Science  
Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge.  ...  In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data.  ...  These Lidar based methods focus on the parameters acquisition of group level rather than individual crop level, which cannot fully meet the needs of precision phenotypic traits extraction.  ... 
doi:10.3389/fpls.2018.00866 pmid:29988466 pmcid:PMC6024748 fatcat:rzo6fhanzjcj5cffyyzg4tcztm

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

Sheng Wu, Weiliang Wen, Boxiang Xiao, Xinyu Guo, Jianjun Du, Chuanyu Wang, Yongjian Wang
2019 Frontiers in Plant Science  
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.  ...  Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies.  ...  Accuracy Analysis of Phenotypic Traits Using the Extracted Skeleton Using the Extracted Skeleton The extracted skeleton of maize plant contains segmentation of organs and semantic meaning of each organ  ... 
doi:10.3389/fpls.2019.00248 pmid:30899271 pmcid:PMC6416182 fatcat:aehe2qccjbh63cwbtyl6krfwjy

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Suresh Thapa, Feiyu Zhu, Harkamal Walia, Hongfeng Yu, Yufeng Ge
2018 Sensors  
We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants.  ...  Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived.  ...  The authors would like to acknowledge the staff members at the University of Nebraska-Lincoln's Greenhouse Innovation Center for their assistance in data collection.  ... 
doi:10.3390/s18041187 pmid:29652788 pmcid:PMC5948551 fatcat:yqyuwff7xjh7pmu7crh6bozd7i

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

Shichao Jin, Yanjun Su, Yongguang Zhang, Shilin Song, Qing Li, Zhonghua Liu, Qin Ma, Yan Ge, LingLi Liu, Yanfeng Ding, Frédéric Baret, Qinghua Guo
2021 Plant Phenomics  
Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.  ...  Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.  ...  TLS data were processed with a series of standard steps, including clipping, noise removal, filtering, normalization, individual plant segmentation, and stem-leaf segmentation ( Figure 2 ).  ... 
doi:10.34133/2021/9895241 pmid:34557676 pmcid:PMC8441379 fatcat:6kqglwp6erc3fiao236chhxhby

Evaluating maize phenotype dynamics under drought stress using terrestrial lidar

Yanjun Su, Fangfang Wu, Zurui Ao, Shichao Jin, Feng Qin, Boxin Liu, Shuxin Pang, Lingli Liu, Qinghua Guo
2019 Plant Methods  
The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively.  ...  The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth  ...  angle, leaf length, and leaf width) from terrestrial lidar data.  ... 
doi:10.1186/s13007-019-0396-x pmid:30740137 pmcid:PMC6360786 fatcat:26o56qiqqjapbk73kn5rw3ugv4

In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse

Abbas Atefi, Yufeng Ge, Santosh Pitla, James Schnable
2019 Computers and Electronics in Agriculture  
In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants.  ...  In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare  ...  Jin et al. (2018) used deep learning and regional growth algorithms for individual maize segmentation from terrestrial LiDAR data.  ... 
doi:10.1016/j.compag.2019.104854 fatcat:ku47tqb4srg2risrs4ukqq7rqi

Making Use of 3D Models for Plant Physiognomic Analysis: A Review

Abhipray Paturkar, Gourab Sen Sen Gupta, Donald Bailey
2021 Remote Sensing  
Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers.  ...  In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors.  ...  Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: There is no any conflicts of interest.  ... 
doi:10.3390/rs13112232 fatcat:y65narwvcnc2tmwazk6ci23se4

Lidar Boosts 3D Ecological Observations and Modelings: A review and perspective

Qinghua Guo, Yanjun Su, Tianyu Hu, Hongcan Guan, Shichao Jin, Jing Zhang, Xiaoxia Zhao, Kexin Xu, Dengjie Wei, Maggi Kelly, Nicholas Coops
2020 IEEE Geoscience and Remote Sensing Magazine  
[103] , [165] extracted individual maize height from TLS data with a high agreement with manual measurements (R 2 > 0.91) and systematically evaluated the accuracy of phenotypes at stem and leaf levels  ...  [103] further demonstrated a deep learningbased method for the separation of stem and leaves of an individual maize plant from terrestrial lidar data. Similarly, Malambo et al.  ... 
doi:10.1109/mgrs.2020.3032713 fatcat:vot6c4ceabectd6xcpgev5duyu

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Shunfu Xiao, Honghong Chai, Ke Shao, Mengyuan Shen, Qing Wang, Ruili Wang, Yang Sui, Yuntao Ma
2020 Remote Sensing  
Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length.  ...  An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant.  ...  Lei [55] used UAV with lidar to inverse leaf area index of maize and studied the effect of leaf occlusion on inversing leaf area index of maize.  ... 
doi:10.3390/rs12020269 fatcat:qixruomrijdhtiaulyfwggejh4

Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction

Jizhang Wang, Yun Zhang, Rongrong Gu
2020 Agriculture  
from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction  ...  Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies.  ...  Jin [40] used LiDAR FARO Focus 3D X 330 HDR to get maize point cloud data, and realized stem-leaf segmentation and phenotypic trait extraction in an experiment carried out in the Botany Garden.  ... 
doi:10.3390/agriculture10100462 fatcat:qwl3yzuxurdahlxrv6yz72zdhi

Robotic Detection and Grasp of Maize and Sorghum: Stem Measurement with Contact

Abbas Atefi, Yufeng Ge, Santosh Pitla, James Schnable
2020 Robotics  
The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping.  ...  An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system.  ...  Conflicts of Interest: The authors declare no conflict of interest. Robotics 2020, 9, 58  ... 
doi:10.3390/robotics9030058 fatcat:gqifoix4g5gcxeaeiumh4a46se

Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates

Yongjian Wang, Weiliang Wen, Sheng Wu, Chuanyu Wang, Zetao Yu, Xinyu Guo, Chunjiang Zhao
2018 Remote Sensing  
Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction.  ...  Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits.  ...  and manual measurement were used to obtain the phenotypic traits of maize plants.  ... 
doi:10.3390/rs11010063 fatcat:vq5p5nl6i5c5xikrqjk7vpnlki

Imaging technologies for plant high-throughput phenotyping: a review

Yong ZHANG, Naiqian ZHANG
2018 Frontiers of Agricultural Science and Engineering  
to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics.  ...  Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants.  ...  This article is a review and does not contain any studies with human or animal subjects performed by any of the authors.  ... 
doi:10.15302/j-fase-2018242 fatcat:4q6ibs64f5hufamuv3jtsixkbu

Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review

Qinlin Xiao, Xiulin Bai, Chu Zhang, Yong He
2021 Journal of Advanced Research  
Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance  ...  Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics.  ...  Furthermore, light detection and ranging (LIDAR) contributed in wheat GWAS, which was used to investigate the genetic response to temperature fluctuations during stem elongation [100] .  ... 
doi:10.1016/j.jare.2021.05.002 pmid:35003802 pmcid:PMC8721248 fatcat:vkwm6nbam5bmll3c5z73cvuhzu
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