农业机械学报2024，Vol.55Issue(4)：184-192,230,10.DOI:10.6041/j.issn.1000-1298.2024.04.018
基于神经辐射场的苗期作物三维建模和表型参数获取
Three-dimensional Reconstruction and Phenotype Parameters Acquisition of Seeding Vegetables Based on Neural Radiance Fields
摘要
Abstract
Accurate and efficient reconstruction of seedling crop structures is crucial for obtaining phenotype parameters.The traditional method for 3D reconstruction based on the structure from motion and multi-view stereo(SFM-MVS)algorithm,which had high reconstruction accuracy and high computional cost.It was difficult to meet the demand for rapid acquisition of phenotype parameters.A system for acquiring phenotype parameters and creating 3D models of seedling crops was proposed by using neural radiance fields(NeRF).The system utilized smart phone to capture RGB images of the objects from various viewpoints and constructed the 3D model through the NeRF algorithm.The algorithms of line fitting and region growing in point cloud library(PCL)were used to automatically segment the plants.Additionally,the algorithms of distance-minimum traversal,circle fitting,and triangulation were used to measure phenotype parameters such as plant height,stem diameter,and leaf area.To assess the reconstruction efficiency and accuracy of phenotype parameter measurement,seedling plants of pepper,tomato,strawberry and epipremnum aureum were selected as subjects.The reconstruction results were compared by using the NeRF and the SFM-MVS algorithm.The results indicated that both methods were capable of achieving superior reconstruction outcomes.The root mean square errors of the point-to-point distances of each seedlings were only 0.128 cm to 0.359 cm.But in terms of speed,this method improved the reconstruction speed by an average of 700％compared with the SFM-MVS method.The method used to extract plant height and stem diameter of chili pepper seedlings had a coefficient of determination(R2)of 0.971 and 0.907,respectively.The root mean square error(RMSE)was 0.86 cm and 0.017 cm,respectively.The R2 of the leaf area extracted from the plants at seedling stage ranged from 0.909 to 0.935,and the RMSE ranged from 0.75 cm2 to 3.22 cm2,indicating a high level of accuracy in measurement.The proposed method can significantly speed up 3D reconstruction and acquisition of phenotype parameters.This would provide a more efficient technical means for vegetable breeding and seedling selection.关键词
苗期作物/三维重建/神经辐射场/表型参数/叶面积Key words
seedling crop/three-dimensional reconstruction/neural radiance fields/phenotype parameters/leaf area分类
信息技术与安全科学引用本文复制引用
朱磊,江伟,孙伯颜,柴明堂,李赛驹,丁一民..基于神经辐射场的苗期作物三维建模和表型参数获取[J].农业机械学报,2024,55(4):184-192,230,10.基金项目
宁夏回族自治区重点研发计划项目(2021BBF02027)、国家自然科学基金项目(52269015)和宁夏自然科学基金优秀青年项目(2023AAC05013) （2021BBF02027）
