Crop phenotypic parameters extraction method based on skeleton extraction algorithm

In view of the problem that obtaining maize plant type parameters mainly depends on the artificial measurement at present in China, which is time-consuming and strenuous, a method to extract phenotypic parameters based on range images was proposed.Based on the corn crop, the algorithm first carries on the single pixel refinement and detection of feature points after denoising the binary image of crops; then stalk and leaves are separated according to the characteristics of the crops corresponding the space coordinates of pixels; according to the least square thought, the objective function and fitness function of genetic algorithm are improved to get the space curves which fit the space discrete points, finally the crop phenotypic parameters could be extracted through the space curves which are also the smooth skeleton of the stem and leaves.Compared with literature 9, field experimental results of the improved algorithm showed that the error of plant height is reduced by about 35%, the leaf length error reduced by 70% and the leaf angle error reduced by 20%.Experimental results demonstrate that the algorithm effectively improves the phenotypic parameters measurement precision, and provides the technical support for 3D model reconstruction of crops.