Branch segmentation and phenotype extraction of apple trees based on improved Laplace algorithm

Abstract

Phenotypic traits of crops reflect their physiological characteristics and provide a theoretical basis for predicting their growth. The 3D point cloud has a direct and accurate rendering ability, which has been widely used in phenotype extraction, especially with the help of accurate segmentation techniques. However, the inherent discrete nature of point clouds makes accurate organ segmentation an ongoing challenge in the field. In this study, we propose a tree phenotype acquisition method based on point cloud registration and skeleton segmentation. First, the Convex Hull-indexed Gaussian Mixture Model (CH-GMM) is employed to register the ground and aerial point cloud data. Then, a Laplace-multi-scale adaptive algorithm (LMSA) was proposed to obtain the crop skeleton structure, on the basis of which four phenotypic parameters, namely, plant height, crown width, branching number, and initial branching height, were extracted for fruit trees. In addition, the relationship between crown width and the number of branches was explored, where branches included initial, secondary, and tertiary branches. The results show that the proposed CH-GMM algorithm has a rotation error of less than 1.01°, a translation error of less than 10 mm, and a success rate of more than 95 %. The average precision, average recall, average F1 score, and average overall accuracy of the LMSA are 93.7 %, 96.2 %, 92.6 %, and 95.3 %, respectively. Finally, this study found a polynomial and exponential relationship between the number of bifurcations and crown size of fruit trees. The results of this study may provide new ideas for fruit tree phenotype acquisition and phenotype management.