SCAG:A Stratified,Clustered,and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation

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.

A new component maps correction method using variable geometric parameters

Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurately correct the component maps of the model over a wide range.In this paper,a new method for modifying component maps is proposed,this method combines the correction of the scaling factors with the solution process of the off-design working point,and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line,in order to obtain more correction results and guarantee high accuracy in a wider range.The method is validated by taking the main fan of the Adaptive Cycle Engine(ACE),an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft,as an example.The results show that the maximum error between the corrected component maps and the target maps is less than 1%.New possibility for more precise component maps can be realized in this paper.