Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding.Due to the species diversity of plants,existing methods heavily rely on large-scale high-precision manually...Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding.Due to the species diversity of plants,existing methods heavily rely on large-scale high-precision manually annotated data.For self-occluded objects at the grain level,unsupervised methods often prove ineffective.This study proposes IPENS,an interactive unsupervised multi-target point cloud extraction method.It utilizes radi-ance field information to lift 2D masks,segmented by SAM2(Segment Anything Model 2),into 3D space for target point cloud extraction.A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction.On a rice dataset,IPENS achieves a grain-level segmen-tation mean Intersection over Union(mIoU)of 63.72%.For phenotypic trait estimation,it achieves a grain voxel volume coefficient of determination R^(2)=0.7697(Root Mean Square Error,RMSE=0.0025),leaf surface area R^(2)=0.84(RMSE=18.93),and leaf length and width prediction accuracies of R^(2)=0.97 and R^(2)=0.87(RMSE=1.49 and 0.21).On a wheat dataset,IPENS further improves segmentation performance to a mIoU of 89.68%,with exceptional phenotypic estimation results:panicle voxel volume R^(2)=0.9956(RMSE=0.0055),leaf surface area R^(2)=1.00(RMSE=0.67),and leaf length and width predictions reaching R^(2)=0.99 and R^(2)=0.92(RMSE=0.23 and 0.15).Without requiring annotated data,IPENS rapidly extracts grain-level point clouds for multiple targets within 3 min using single-round image interactions.These features make IPENS a high-quality,non-invasive phenotypic extraction solution for rice and wheat,offering significant potential to enhance intelligent breeding.展开更多
基金supported by the National Key Research and Development Program of China(Grant Number 2023YFD1901003)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant XDA28120402).
文摘Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding.Due to the species diversity of plants,existing methods heavily rely on large-scale high-precision manually annotated data.For self-occluded objects at the grain level,unsupervised methods often prove ineffective.This study proposes IPENS,an interactive unsupervised multi-target point cloud extraction method.It utilizes radi-ance field information to lift 2D masks,segmented by SAM2(Segment Anything Model 2),into 3D space for target point cloud extraction.A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction.On a rice dataset,IPENS achieves a grain-level segmen-tation mean Intersection over Union(mIoU)of 63.72%.For phenotypic trait estimation,it achieves a grain voxel volume coefficient of determination R^(2)=0.7697(Root Mean Square Error,RMSE=0.0025),leaf surface area R^(2)=0.84(RMSE=18.93),and leaf length and width prediction accuracies of R^(2)=0.97 and R^(2)=0.87(RMSE=1.49 and 0.21).On a wheat dataset,IPENS further improves segmentation performance to a mIoU of 89.68%,with exceptional phenotypic estimation results:panicle voxel volume R^(2)=0.9956(RMSE=0.0055),leaf surface area R^(2)=1.00(RMSE=0.67),and leaf length and width predictions reaching R^(2)=0.99 and R^(2)=0.92(RMSE=0.23 and 0.15).Without requiring annotated data,IPENS rapidly extracts grain-level point clouds for multiple targets within 3 min using single-round image interactions.These features make IPENS a high-quality,non-invasive phenotypic extraction solution for rice and wheat,offering significant potential to enhance intelligent breeding.
