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During the whole crop growing period and high-precision breeding process, measuring crop plant type parameters to achieve its phenotypic analysis is one of the important link. In view of the problem that the maize plant type parameters are obtained mainly by artificial field measurement in China at present, and it is high labor intensity and time-consuming. Thus a rapid and nondestructive measurement method of maize plant type parameters was proposed based on the photonics mixer device (PMD) camera with improved skeleton extraction algorithm. Firstly, the RGB pseudo color depth and distance information of depth image were used, and the depth of the image skeleton extraction was obtained by the improved skeleton extraction algorithm without complex background interference. Secondly, the binary skeleton image per corn plant was got by taking advantage of the improved corner detection classification algorithm for extracting skeleton image feature points. Finally, the feature points in the skeleton image were corresponded with depth images. Three kinds of corn plant type parameters: plant height, stem diameter and leaf angle were calculated by using mathematics method combined with space geometry feature point. Field test of the method in the practical application environment showed that the plant height could be measured at the seedling stage of maize plant, the correlation coefficient ( r ) of maize plant type parameters between the measured results by using the proposed method and the results measured artificially was 0.986, and the maximum relative error was less than 2 cm. Farmland crops breeding resistance analysis also showed that maize plant type parameters and lodging resistance had significant correlation. The results provide technical support for the inversion of phenotypic analysis of crop breeding.