摘要
为满足高通量作物表型分析需求,提升三维点云重建效率和精度,该研究针对不同作物、不同生育时期、不同植株部位(地上部和根系),基于研发的多视角自动成像系统和SFM(structure from motion)-MVS(multi-view stereo)算法,采用不同视角和不同相机数获取的图像重建作物三维点云,通过重建效率和精度(Hausdorff距离)评估,以及基于点云提取表型参数(株高、幅宽、凸包体积和总表面积)的可靠性评价,优化作物三维点云重建策略。结果显示,对于结构相对稀松、遮挡较少的盆栽植株(苗期、蕾薹期、盛花期、成熟期油菜)、结构相对紧凑、遮挡较多的植株地上部(花铃期棉花、抽穗期水稻、拔节期和灌浆期小麦)以及器官密集、遮挡严重且有较多细长结构的地上部和根系(分蘖期小麦和成熟期水稻地上部、成熟期玉米和油菜根系),分别采用3~4、6和10个相机为其最优重建策略(Hausdorff距离小于或接近0.20 cm,且重建时长和Hausdorff距离归一化值之和最小)。采用不少于4个相机获取的图像重建作物三维点云,可提取较为可靠的表型参数(决定系数R2>0.90,相对均方根误差RRMSE≤9%)。该研究提出的最优重建策略平衡了自动成像系统构建成本、三维重建效率和精度以及适用植株复杂程度,为实现多种作物高效、低成本、高精度三维重建和表型参数提取提供了重要依据。
Digital photography has provided an economic and convenient way to generate three-dimensional(3D)point clouds for high-throughput crop phenotyping in plant 3D reconstruction.Manual acquisition of multi-view images is time-consuming and labor-intensive,as hundreds of images can be required to generate high-quality 3D point clouds.Multi-view automatic imaging systems can be expected to significantly reduce the cost of labor and time.However,the currently available systems cannot balance the number of cameras,the cost of equipment,the efficiency and accuracy of the 3D reconstruction,as well as the applicability to the complexity of plants.In this study,the automatic imaging system was developed to acquire multi-view images of different crops at various growth stages.3D point clouds were then generated from the multi-view images acquired by different strategies(i.e.,different imaging perspectives)and the number of cameras(1,2,3,4,6,and 10 camera/cameras)using the SFM(structure from motion)-MVS(multi-view stereo)algorithm.Statistical filtering was used to remove the noise and outliers.The non-plant 3D point clouds were removed using RGB colors.The reconstructed 3D models were aligned to the reference 3D models using the ICP(Iterative Closest Point)algorithm.Hausdorff distance between the two models was calculated to combine the reconstruction time for the evaluation of the precision and efficiency of 3D point clouds reconstruction with different strategies.The 3D point cloud reconstruction strategies were optimized for different crops at different growth stages using the efficiency and precision of reconstruction.The optimization criteria:the average Hausdorff distance was less than or close to 0.20 cm,with the minimum normalization of reconstruction time and Hausdorff distance.The reliability of extracting phenotypic parameters(height,width,convex hull volume,and total surface area of plants)was evaluated from the 3D models reconstructed with different strategies.Plant height and the maximum width were calculated directly using the reconstructed 3D point clouds.The Convex Hull was generated to calculate the Convex Hull Volume.Poisson-disk sampling(Explicit radius=0.5,and MonteCarlo oversampling=20)and Ball Pivoting algorithm were used to reconstruct the 3D mesh model.The total surface area of plants was then calculated using the generated 3D mesh model.The results showed that the time of 3D reconstruction increased,whereas,the Hausdorff distance decreased with the increase of the number of cameras.According to the optimization criteria,three to four cameras were used as the optimal reconstruction strategy for rapeseed at seedling,bolting,flowering,and mature stages,while six cameras were used as the optimal reconstruction strategy for rice at the heading stage,cotton at flowering and boll-setting stages,as well as wheat at jointing and filling stages,and ten cameras were as the optimal reconstruction strategy for wheat at tillering stage,rice at maturity stage,maize roots and rapeseed roots at maturity stage.The reliable phenotypic parameters were obtained using the 3D reconstruction system with no less than four cameras(determination coefficient R2>0.90;relative root mean square error RRMSE≤9%).The optimal reconstruction strategy can greatly contribute to the high-efficiency,low-cost,and high-precision 3D reconstruction and phenotypic parameter extraction of multiple crops at different growth stages.
作者
李百明
吴茜
吴劼
张美娜
李华勇
于堃
曹静
张伟欣
曹宏鑫
张文宇
LI Baiming;WU Qian;WU Jie;ZHANG Meina;LI Huayong;YU Kun;CAO Jing;ZHANG Weixin;CAO Hongxin;ZHANG Wenyu(School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China;Institute of Agricultural Information,Jiangsu Academy of Agricultural Sciences,Nanjing 210014,China;Plant Phenomics Research Center,Academy for Advanced Interdisciplinary Studies,Nanjing Agricultural University,Nanjing 210095,China;Institute of Germplasm Resources and Biotechnology,Jiangsu Academy of Agricultural Sciences,Nanjing 210014,China;Jiangsu Academy of Agricultural Sciences Wuxi Branch/Wuxi Academy of Agricultural Sciences,Wuxi 214174,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2023年第9期161-171,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划项目(2022YFD2001001)
国家自然科学基金项目(32201664、31871522、31801263)
江苏省自然科学基金项目(BK20200277)
江苏省重点研发计划项目(BE2022351)
江苏省农业自主创新资金项目(CX(21)2042)。
关键词
植物
表型
多视角立体视觉
图像自动采集
三维重建
点云模型
plant
phenotype
multi-view stereo vision
automatic image acquisition
3D reconstruction
point cloud model
