The Experts below are selected from a list of 163095 Experts worldwide ranked by ideXlab platform
Alexis Comar - One of the best experts on this subject based on the ideXlab platform.
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high throughput phenotyping of Plant Height comparing unmanned aerial vehicles and ground lidar estimates
Frontiers in Plant Science, 2017Co-Authors: Simon Madec, Fred Baret, G. Colombeau, Maximilian Hemmerle, Daniel Dutartre, S Jezequel, B. Solan, Samuel Thomas, Alexis ComarAbstract:The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide Plant Height estimates as a high-throughput Plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phenomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the Plant Height can be estimated. Plant Height first defined as the z value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of Plant Height (RMSE=3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion Plant Height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H²>0.90) were found for both techniques when lodging was not present. The dynamics of Plant Height shows that it carries pertinent information regarding the period and magnitude of the Plant stress. Further, the date when the maximum Plant Height is reached was found to be very heritable (H²>0.88) and a good proxy of the flowering stage. Finally, the capacity of Plant Height as a proxy for total above ground biomass and yield is discussed.
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High-throughput phenotyping of Plant Height: Comparing unmanned aerial vehicles and ground lidar estimates
Frontiers in Plant Science, 2017Co-Authors: Sten Madec, Susie Thomas, Fred Baret, G. Colombeau, Maximilian Hemmerle, Daniel Dutartre, S Jezequel, B. Solan, Alexis ComarAbstract:The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide Plant Height estimates as a high-throughput Plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the Plant Height can be estimated. Plant Height first defined as the z value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of Plant Height (RMSE=3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion Plant Height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H²>0.90) were found for both techniques when lodging was not present. The dynamics of Plant Height shows that it carries pertinent information regarding the period and magnitude of the Plant stress. Further, the date when the maximum Plant Height is reached was found to be very heritable (H²>0.88) and a good proxy of the flowering stage. Finally, the capacity of Plant Height as a proxy for total above ground biomass and yield is discussed.
Lijuan Qiu - One of the best experts on this subject based on the ideXlab platform.
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Identification of Quantitative Trait Loci Underlying Plant Height and Seed Weight in Soybean
The Plant Genome, 2013Co-Authors: Yu-lin Liu, Jochen C. Reif, Michael Florian Mette, Zhangxiong Liu, Bo Liu, Shan-shan Zhang, Long Yan, Ru-zhen Chang, Lijuan QiuAbstract:For clarifying the genetic base of the variation of yieldrelated traits in soybean [Glycine max (L.) Merr.], we mapped quantitative trait loci (QTLs) for Plant Height and seed weight using a recombinant inbred line population derived from a cross between Chinese elite line Zhongpin03-5373 and cultivar Zhonghuang13. We detected 11 QTLs for Plant Height and 18 QTLs for seed weight across six diverse environments. These included three pairs of Plant Height- vs. seed weight-related QTLs located in close proximity to each other, with two pairs, qPH-7 vs. qSW-7-2 and qPH-19-2 vs. qSW-19, sharing the same direction of additive effects. Individual QTLs explained 2.02 to 47.60% of the variation in Plant Height and 2.13 to 14.35% in seed weight. Two and five of the major QTLs discovered for Plant Height and seed weight, respectively, that were stable across environments in our study have been reported previously. Among them, four QTLs, qPH-13, qSW-11, qSW-12-2, and qSW-18, were not involved in digenic epistatic interaction in our biparental population, indicating that these QTLs will be useful for marker-assisted selection and should be targeted for the future identification of candidate genes. Moreover, eight QTLs for both Plant Height and seed weight were newly identified in our population.
Malay C Saha - One of the best experts on this subject based on the ideXlab platform.
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Quantitative Trait Loci (QTL) Underlying Biomass Yield and Plant Height in Switchgrass
Bioenergy Research, 2014Co-Authors: Desalegn D. Serba, E. Charles Brummer, Guillaume Daverdin, Katrien M Devos, Joseph H. Bouton, Malay C SahaAbstract:Switchgrass (Panicum virgatum L.) biomass yield and feedstock quality improvement are priority research areas for bioenergy feedstock development. Identification of quantitative trait loci (QTL) underlying these traits and of trait-linked markers for application in marker-assisted selection (MAS) is of paramount importance in facilitating switchgrass breeding. Detection of QTL for biomass yield and Plant Height was conducted on parental linkage maps constructed using a heterozygous pseudo-F1 population derived from a cross between lowland Alamo genotype AP13 and upland Summer genotype VS16. QTL analysis was performed with composite interval mapping. Four QTL for biomass yield and five QTL for Plant Height were identified using best linear unbiased predictors across ten and eight environments, respectively. The phenotypic variability explained (PVE) by QTL detected in the across environments analysis ranged from 4.9 to 12.4 % for biomass yield and 5.1 to 12.0 % for Plant Height. A total of 34 and 38 main effect QTL were detected for biomass yield and Plant Height, respectively, when data from each environment were analyzed separately. The PVE by individual environment QTL ranged from 3.3 to 15.3 % for biomass yield and from 4.3 to 17.4 % for Plant Height. In addition, 60 and 51 epistatic QTL were detected for biomass yield and Plant Height, respectively. Significant QTL by environment interactions were detected for QTL mapped in eight genomic regions for each of the two traits. Seven QTL affected both traits and may represent pleiotropic loci. Overall, 11 genomic regions were identified that were important in controlling biomass yield and/or Plant Height in switchgrass. The markers linked to the main effect and epistatic QTL may be used in MAS to maximize selection gain in switchgrass breeding, leading to a faster development of better biofuel cultivars.
Jochen C. Reif - One of the best experts on this subject based on the ideXlab platform.
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Mapping dynamic QTL for Plant Height in triticale.
BMC genetics, 2014Co-Authors: Tobias Würschum, Wenxin Liu, Lucas Busemeyer, Matthew R. Tucker, Jochen C. Reif, Elmar A. Weissmann, Volker Hahn, Arno Ruckelshausen, Hans Peter MaurerAbstract:Background: Plant Height is a prime example of a dynamic trait that changes constantly throughout adult development. In this study we utilised a large triticale mapping population, comprising 647 doubled haploid lines derived from 4 families, to phenotype for Plant Height by a precision phenotyping platform at multiple time points. Results: Using multiple-line cross QTL mapping we identified main effect and epistatic QTL for Plant Height for each of the time points. Interestingly, some QTL were detected at all time points whereas others were specific to particular developmental stages. Furthermore, the contribution of the QTL to the genotypic variance of Plant Height also varied with time as exemplified by a major QTL identified on chromosome 6A. Conclusions: Taken together, our results in the small grain cereal triticale reveal the importance of considering temporal genetic patterns in the regulation of complex traits such as Plant Height.
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Identification of Quantitative Trait Loci Underlying Plant Height and Seed Weight in Soybean
The Plant Genome, 2013Co-Authors: Yu-lin Liu, Jochen C. Reif, Michael Florian Mette, Zhangxiong Liu, Bo Liu, Shan-shan Zhang, Long Yan, Ru-zhen Chang, Lijuan QiuAbstract:For clarifying the genetic base of the variation of yieldrelated traits in soybean [Glycine max (L.) Merr.], we mapped quantitative trait loci (QTLs) for Plant Height and seed weight using a recombinant inbred line population derived from a cross between Chinese elite line Zhongpin03-5373 and cultivar Zhonghuang13. We detected 11 QTLs for Plant Height and 18 QTLs for seed weight across six diverse environments. These included three pairs of Plant Height- vs. seed weight-related QTLs located in close proximity to each other, with two pairs, qPH-7 vs. qSW-7-2 and qPH-19-2 vs. qSW-19, sharing the same direction of additive effects. Individual QTLs explained 2.02 to 47.60% of the variation in Plant Height and 2.13 to 14.35% in seed weight. Two and five of the major QTLs discovered for Plant Height and seed weight, respectively, that were stable across environments in our study have been reported previously. Among them, four QTLs, qPH-13, qSW-11, qSW-12-2, and qSW-18, were not involved in digenic epistatic interaction in our biparental population, indicating that these QTLs will be useful for marker-assisted selection and should be targeted for the future identification of candidate genes. Moreover, eight QTLs for both Plant Height and seed weight were newly identified in our population.
S Jezequel - One of the best experts on this subject based on the ideXlab platform.
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high throughput phenotyping of Plant Height comparing unmanned aerial vehicles and ground lidar estimates
Frontiers in Plant Science, 2017Co-Authors: Simon Madec, Fred Baret, G. Colombeau, Maximilian Hemmerle, Daniel Dutartre, S Jezequel, B. Solan, Samuel Thomas, Alexis ComarAbstract:The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide Plant Height estimates as a high-throughput Plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phenomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the Plant Height can be estimated. Plant Height first defined as the z value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of Plant Height (RMSE=3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion Plant Height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H²>0.90) were found for both techniques when lodging was not present. The dynamics of Plant Height shows that it carries pertinent information regarding the period and magnitude of the Plant stress. Further, the date when the maximum Plant Height is reached was found to be very heritable (H²>0.88) and a good proxy of the flowering stage. Finally, the capacity of Plant Height as a proxy for total above ground biomass and yield is discussed.
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High-throughput phenotyping of Plant Height: Comparing unmanned aerial vehicles and ground lidar estimates
Frontiers in Plant Science, 2017Co-Authors: Sten Madec, Susie Thomas, Fred Baret, G. Colombeau, Maximilian Hemmerle, Daniel Dutartre, S Jezequel, B. Solan, Alexis ComarAbstract:The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide Plant Height estimates as a high-throughput Plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the Plant Height can be estimated. Plant Height first defined as the z value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of Plant Height (RMSE=3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion Plant Height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H²>0.90) were found for both techniques when lodging was not present. The dynamics of Plant Height shows that it carries pertinent information regarding the period and magnitude of the Plant stress. Further, the date when the maximum Plant Height is reached was found to be very heritable (H²>0.88) and a good proxy of the flowering stage. Finally, the capacity of Plant Height as a proxy for total above ground biomass and yield is discussed.
