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Maize is one of the most widely distributed crops in the world, ranking third only to wheat and rice. The plant height, stalk diameter and leaf area of maize are closely related to its yield, the leaf projection area and leaf stem angle have an direct effect on utilization of light energy to maize plants, the number of leaves is the indicator of the overground part biomass, the parameters such as minimum enveloping box volume of single leaf, leaf perimeter, leaf projection width, leaf projection length and so on directly affect the spatial distribution of leaves, therefore, dynamic monitoring of these traits is particularly important. However, the traditional measurement of these traits is timeconsuming, costly, subjective and destructive. To achieve the dynamic, rapid, accurate and nondestructive outdoor measurement of maize plant height, stalk diameter, leaf area, the number of leaves, leaf stem angle and so on, threedimensional (3D) models of tassel stage maize plants were reconstructed by using structure from motion (SfM) algorithm. An autonomous crawler phenotyping robot was used for acquiring multiview maize plants images along the maize crop rows outdoors. The robot could work continuously four hours at speed of 0.1m/s and would acquire about 700 stable images for a single camera. The 3D point cloud data were obtained using the multiview images in the Visual SFM software. The 3D point cloud data were preprocessed and some morphological traits such as maize plant height, minimum enveloping box volume of single plant, stalk diameter, the number of leaves, leaf perimeter, leaf area, minimum enveloping box volume of single leaf, leaf projection area, leaf projection width, leaf projection length and leaf stem angle were extracted in the Visual Studio 2013 plus PCL platform. Compared with the manual measurement, the mean absolute percentage errors (MAPE) for plant height, stalk diameter and leaf area were 3.163%, 4.760% and 19.102%, respectively. The root mean square error (RMSE) for plant height, stalk diameter and leaf area were 3.557cm, 1.540mm and 48.163cm2, respectively. The R2 for plant height, stalk diameter and leaf area were 0.970, 0.842 and 0.901, respectively. The results showed that 3D reconstruction method based on SfM algorithm was suitable for outdoor measurement. In addition, the maize plants were divided into low overground part biomass maize and high overground part biomass maize by the fresh weight of the overground part plant, meanwhile, the plant trait such as height, minimum enveloping box volume of single plant, stalk diameter and the number of leaves were extracted with segmented point cloud data to calculate the P value by single factor analysis of variance. The measured P values were 0.0003, 0.0004, 0.3170 and 0.2415, respectively, and the results proved that the traits of plant height and minimum enveloping box volume of single plant were able to distinguish the low overground part biomass maize and high overground part biomass maize evidently. The research result provided scientific researchers and crop breeders a new phenotyping method for measuring crop traits to some extent.