基于立体视觉的玉米雄穗三维信息提取  

Three dimensional information extraction from maize tassel based on stereoscopic vision

作　　者：韩东 杨贵军[1] 杨浩[1] 邱春霞[2] 陈明杰 温维亮[1] 牛庆林 杨文攀[1,3] Han Dong;Yang Guijun;Yang Hao;Qiu Chunxia;Chen Mingjie;Wen Weiliang;Niu Qinglin;Yang Wenpan(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P.R.China,Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;College of Geomatics,Xi＇an University of Science and Technology,Xi＇an 710054,China;School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]农业部农业遥感机理与定量遥感重点实验室北京农业信息技术研究中心,北京100097 [2]西安科技大学测绘科学与技术学院,西安710054 [3]河南理工大学测绘与国土信息工程学院,焦作454000

出　　处：《农业工程学报》2018年第11期166-173,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划课题(2016YFD0300602);北京市自然科学基金(6182011);国家自然科学基金(41401477;61661136003);北京市农林科学院创新能力建设专项(KJCX20170423)

摘　　要：玉米雄穗的表型信息对玉米育种研究具有重要的参考意义。该研究以自动获取玉米雄穗三维表型信息为目的。通过对雄穗样本进行多视角摄影处理来重建其三维模型。对重建的三维点云数据运用基于密度聚类的方法统计其分枝数信息,运用Delaunays三角网方法计算其外包络体积信息,并基于点云信息对雄穗主轴和最大穗冠的结构参数进行计算,同时定义了相关表型参数。用实测结果验证计算结果:分枝数统计结果的最大绝对误差为2,RMSE(root mean square error)为1.03,n RMSE(normalized root mean square error)为0.05;主轴长度,主轴最大/最小直径,最大穗冠高度和最大穗冠直径的R^2分别为0.99,0.82,0.83,0.97和0.93,均达到极显著相关水平。研究提出的相关表型参数和其提取方法在育种研究中具有应用潜力,为田间高通量雄穗信息的快速提取提供了参考。The phenotypic information of maize tassel has important reference significance for maize breeding. In this study, 17 maize tassel samples were collected at the harvest stage for the purpose of obtaining the three-dimensional phenotypic information of tassels automatically. The samples were collected by the experimenter from the field and then the photogrammetry was taken indoors with them. The overlapping rate of each image is greater than 60%, and finally 72 multi-view photos were obtained for each tassel sample. Acquired tassel multi-view photos were used for three-dimensional modeling processing in the VisualS FM software. Since the reconstructed tassel three-dimensional model includes tassels and background plates, there are a large number of noise points. Therefore, it cannot be directly used for extracting phenotypic information. In this study, the three-dimensional model of tassel was first preprocessed, including point cloud thinning, noise removal, background plate separation and other steps. Then, the three-dimensional model results obtained by program were used to compute tassel phenotype information. For the obtained tassel samples, the number of branches, the main axis length, the maximum diameter of the main axis, the minimum diameter of the main axis, the maximum canopy diameter, the maximum canopy height and other information were manually measured. The artificially acquired phenotypic information is used as a verification dataset for the results of the phenotypic information calculated with the program. The number of tassel branches, tassel volume, main axis information（length, maximum diameter, minimum diameter of main axis）, information of maximum canopy（diameter and height）, total projected area and other parameters of information were calculated using computer methods for statistics. The statistics of the number of branches use density-based clustering method. The algorithm divides a region with sufficient density into clusters and finds arbitrarily shaped clusters in the noisy spati

关 键 词：作物 机器视觉 三维重建 玉米雄穗 数码相机 雄穗表型信息 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]