3D reconstruction of strawberry leaves based on contour segmentation

Abstract: Based on the data measured by instruments, the plant three-dimensional reconstruction is an important part of the plant digital research. Through the establishment of the plant three-dimensional model, not only can the growth rule of the plants in real environment be researched by measuring the crop parameters quickly, but also the spatial structure of local visual scene of plant can be explored by analyzing plant leaf distribution feature. In order to construct three-dimensional structure of situ strawberry plants precisely for further study in plant spatial structure, the paper took strawberry plants of elevated cultivation environment as the research object and proposed a three-dimensional strawberry canopy morphology reconstruction algorithm based on multi-source image contour segmentation. We divided the main algorithm into 3 parts: multi-source image pre-processing, coarse segmentation of intensity image and model fitting. To make full use of the advantages of color images in color segmentation, color image and intensity image in different resolution are registered and merged. The paper used the improved multi-source information fusion algorithm of strawberry plants based on feature. By using feature-based multi-source information fusion algorithm, feature information of each source image is extracted and analyzed. Then the invariable feature points are selected. Later by checking the similarity of the feature points and adding appropriate parameter constraints, the registration information is obtained. Multi-source image mapping relationship is established by applying registration information. Then by fusing the preprocessed images, the information complement of color image and intensity map is realized and finally the intensity image to be split is obtained by the image preprocessing. Calculating local center of the vector field of intensity image to be segmented means calculating vector field for each pixel. Then the largest local pixels are picked out. Later the vector direction of each pixel is divided into 3 categories by a symbolic function. Clustering potential scattering point set in an array and the local control point are determined by applying a given threshold. Finally by applying the active contour model of the parameters and the central control point to the segmented intensity image we get a coarse segmentation image of the blade. A method of model reconstruction based on surface fitting was proposed for further processing. Intensity image's segmentation contour is regard as the edge contour, and the contour interior point cloud is extracted. The method of region marking is used to mark the used point cloud which belongs to original depth point cloud data, and by checking the number of unmarked point clouds we can know whether the extraction is completed. The plane fitting selection mechanism based on point cloud is designed to compare the minimum mean square deviation of the surface and the plane after fitting, and the optimal fitting model is selected. All the optimal models are displayed in a coordinate system, and the points are colored one by one. Finally, the reconstruction and display of the three-dimensional model of strawberry are finished. To verify the effectiveness of the algorithm, the paper took the distance difference between average single-leaf length and leaf distance as an evaluation index. Experimental results showed that the correction rate of number of blades was 85.6%, that of single-leaf model was 88.4% and that of distance difference was 82.4%. The results can be applied to the spatial position measurement of in situ strawberry plants. The research provides a new method for the construction of plant spatial structure in local vision scenes of agricultural robots.