Three-dimensional(3D)phenotyping is important for studying plant structure and function.Light detection and ranging(LiDAR)has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds.How...Three-dimensional(3D)phenotyping is important for studying plant structure and function.Light detection and ranging(LiDAR)has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds.However,organ-level branch detection remains challenging due to small targets,sparse points,and low signal-to-noise ratios.In addition,extracting biologically relevant angle traits is difficult.In this study,we developed a stratified,clustered,and growing-based algorithm(SCAG)for soybean branch detection and branch angle calculation from LiDAR data,which is heuristic,open-source,and expandable.SCAG achieved high branch detection accuracy(F-score=0.77)and branch angle calculation accuracy(r=0.84)when evaluated on 152 diverse soybean varieties.Meanwhile,the SCAG outperformed 2 other classic algorithms,the support vector machine(F-score=0.53)and density-based methods(F-score=0.55).Moreover,after applying the SCAG to 405 soybean varieties over 2 consecutive years,we quantified various 3D traits,including canopy width,height,stem length,and average angle.After data filtering,we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential,such as the ratio of average angle to height and the ratio of average angle to stem length,which showed greater potential than the well-known ratio of canopy width to height trait.Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening.The algorithm can be applied to other crops,such as maize and tomato.Our dataset,scripts,and software are public,which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.展开更多
基金supported in part by the Science and Technology Innovation 2030-Major Project(2023ZD04034)the Fundamental Research Funds for the Central Universities(KYCYXT2022017 and KYQN2023021)+6 种基金Hainan Yazhou Bay Seed Laboratory(B21HJ1005)Jiangsu Province Key Research and Development Program(BE2023369)the Natural Science Foundation of Jiangsu Province(BK20231469)the National Natural Science Foundation of China(32201656)J.Z.was supported by the Sanya Yazhou Bay Science and Technology City(SCKJ-JYRC-2022-20)J.Wu was supported by the HKU Seed Funding for Strategic Interdisciplinary Research Schemethe Innovation and Technology Fund(funding support to State Key Laboratories in Hong Kong of Agrobiotechnology)of the HKSA R,China.
文摘Three-dimensional(3D)phenotyping is important for studying plant structure and function.Light detection and ranging(LiDAR)has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds.However,organ-level branch detection remains challenging due to small targets,sparse points,and low signal-to-noise ratios.In addition,extracting biologically relevant angle traits is difficult.In this study,we developed a stratified,clustered,and growing-based algorithm(SCAG)for soybean branch detection and branch angle calculation from LiDAR data,which is heuristic,open-source,and expandable.SCAG achieved high branch detection accuracy(F-score=0.77)and branch angle calculation accuracy(r=0.84)when evaluated on 152 diverse soybean varieties.Meanwhile,the SCAG outperformed 2 other classic algorithms,the support vector machine(F-score=0.53)and density-based methods(F-score=0.55).Moreover,after applying the SCAG to 405 soybean varieties over 2 consecutive years,we quantified various 3D traits,including canopy width,height,stem length,and average angle.After data filtering,we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential,such as the ratio of average angle to height and the ratio of average angle to stem length,which showed greater potential than the well-known ratio of canopy width to height trait.Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening.The algorithm can be applied to other crops,such as maize and tomato.Our dataset,scripts,and software are public,which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.
