Density estimation method of mature wheat based on point cloud segmentation and clustering

Abstract

The sustainable development of agriculture needs to rely on precision agricultural technology. The premise and key to realizing precision agriculture is the research on the characteristics of crops in the field. For wheat plants, the ear is the flower or fruit part at the top of the wheat stem, which is one of the important components of yield, and its density can be said to be one of the most important traits. Its number is arguably-one of the most important phenotypic traits. The traditional method of density measurement is manual, which is time-consuming and laborious. Therefore, an efficient and convenient wheat density estimation scheme is needed to provide data support for crop yield monitoring, to achieve a better production management system. Stereo vision is a 3D imaging method that allows rapid measurement of plant structures, and point cloud segmentation is the key to studying the 3D spatial characteristics of plants. In this paper, the three-dimensional point cloud of wheat reconstructed by stereo vision technology is used for segmentation and clustering, and a method for clustering dense wheat rows is proposed. Firstly, a binocular camera was used to record video to reconstruct the wheat point cloud; then, point cloud pre-processing was used to remove noise; then, the octree splitting algorithm and voxel mesh merging algorithm were used to divide the dense wheat, and then clustering algorithm was used to get the point cloud of wheat ears; finally, the relationship model between the number of wheat ears point clouds and the number of wheat plants was established by linear regression analysis with R2 of 0.97. To verify the effectiveness of the algorithm, the actual field measurements and the predicted values of the algorithm were compared, with R2 of 0.93. The density estimation method provides a new method for the study of phenotypic information of crop population information and also provides a reference for nondestructive crop measurements.