A method for obtaining maize phenotypic parameters based on improved QuickShift algorithm

Abstract

Phenotype is an important bridge for studying the mechanism of “genotype-phenotype-environment”, and studying precise measurement of crop individual phenotypic information is of great significance for accelerating breeding processes and assisting in precision agriculture monitoring. The purpose of this study is to develop a clustering algorithm for corn population point clouds to accurately extract the three-dimensional morphology of individual crops. Firstly, this study used drones to obtain field corn data, reconstructed corn plant models based on the SfM algorithm, simplified and rotated point clouds, and extracted crop point clouds using methods based on color and distance thresholds. For density segmentation, this study improved the QuickShift method to segment individual corn crops. Color weights were added to the clustering density calculation process, and the original point cloud was projected onto a horizontal plane to calculate the density clustering core. Then, the remaining points were subjected to QuickShift clustering segmentation in 3D to obtain the point cloud data of individual corn plants. The use of cylindrical fitting and conditional Euclidean clustering algorithms to automatically segment point clouds of stems and leaves has achieved estimation of plant height, stem thickness, minimum bounding box volume per plant, and number of leaves. Finally, the true and measured values of plant height and stem thickness were compared. The results showed that the accuracy of the improved QuickShift method for segmentation was 93.10%, with an average measured plant height of 14.21 cm and an average stem diameter of 0.72 cm. The determination coefficient and root mean square error of the automatic and manual measurement results of plant height are 0.9186 and 0.5785 cm, respectively. The determination coefficients and root mean square errors of the automatic and manual measurement results of stem thickness are 0.8227 and 0.0574 cm, respectively. This study uses an improved QuickShift to cluster and segment corn point cloud data, achieving the segmentation of individual corn plants in the field, providing an automated, efficient, and low-cost solution for accurately measuring crop phenotypic information.