Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lot...Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.展开更多
Tree-ring dating enables gathering necessary knowledge about trees,and it is essential in many areas,including forest management and the timber industry.Tree-ring dating can be conducted on either wood’s clean crosss...Tree-ring dating enables gathering necessary knowledge about trees,and it is essential in many areas,including forest management and the timber industry.Tree-ring dating can be conducted on either wood’s clean crosssections or tree trunks’rough end cross-sections.However,the measurement process is still time-consuming and frequently requires experts who use special devices,such as stereoscopes.Modern approaches based on image processing using deep learning have been successfully applied in many areas,and they can succeed in recognizing tree rings.While supervised deep learning-based methods often produce excellent results,they also depend on extensive datasets of tediously annotated data.To our knowledge,there are only a few publicly available ring image datasets with annotations.We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection.We capture each wood cookie twice,once in the rough form,similar to industrial settings,and then after careful cleaning,that reveals all growth rings.We carefully overlap the images and use them for an automatic ring annotation in the rough data.We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72%and ring level F_(1) score of 0.7348.The data and code are available at https://github.com/wufanyou/growth-ring-detection.展开更多
基金supported by the Foundation for Food and Agriculture Research(602757)USDA-NIFA(2020-68013-32371 and 2024-67013-42449)+3 种基金Department of Energy the Office of Science(BER),U.S.DOE(DESC0020355)the National Science Foundation(IOS-2412930,2417510,and 2412928)the University of Nebraska-Lincoln's Complex Biosystems Graduate Programsupported by the National Science Foundation Graduate Research Fellowship Program under Grant No.2034837.
文摘Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
基金supported by a McIntire Stennis grant(accession No.1012928)to Gazo from the USDA National Institute of Food and Agriculture and a grant from the Purdue University Hardwood Tree Improvement and Regeneration Center and the Foundation for Food and Agriculture Research,United States(Grant ID:602757)to Benes.
文摘Tree-ring dating enables gathering necessary knowledge about trees,and it is essential in many areas,including forest management and the timber industry.Tree-ring dating can be conducted on either wood’s clean crosssections or tree trunks’rough end cross-sections.However,the measurement process is still time-consuming and frequently requires experts who use special devices,such as stereoscopes.Modern approaches based on image processing using deep learning have been successfully applied in many areas,and they can succeed in recognizing tree rings.While supervised deep learning-based methods often produce excellent results,they also depend on extensive datasets of tediously annotated data.To our knowledge,there are only a few publicly available ring image datasets with annotations.We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection.We capture each wood cookie twice,once in the rough form,similar to industrial settings,and then after careful cleaning,that reveals all growth rings.We carefully overlap the images and use them for an automatic ring annotation in the rough data.We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72%and ring level F_(1) score of 0.7348.The data and code are available at https://github.com/wufanyou/growth-ring-detection.
