Wheat canopy leaf dip angle distribution automatic estimation method based on voxel segmentation normal vector algorithm

• Main Authors: GUO TAI, GU YANGYANG, ZHU YAN, CHENG TAO, WANG YONGQING, ZHENG HENGBIAO, CAO WEIXING, JIANG CHONGYA, YAO XIA
• Format: Patent
• Language: Chinese; English
• Published: 26.04.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention discloses a wheat canopy leaf dip angle distribution automatic estimation method based on a voxel segmentation normal vector algorithm. The method comprises the following steps: step 1, obtaining point cloud data of a wheat canopy; step 2, splicing and denoising point clouds; 3, calculating a normal vector of the point cloud; step 4, performing voxelization on the point cloud; 5, segmenting a normal vector by using voxels; step 6, calculating the angle of the voxel; and 7, counting the angles of the voxels, and performing curve fitting calculation to obtain leaf dip angle distribution and an average leaf dip angle. The average blade inclination angle estimated by the method is compared with field measured data, and the feasibility of the algorithm is verified by using a three-dimensional radiation transmission model. According to the method, the problem that the wheat canopy leaf dip angle distribution is influenced by the curvature of the leaf and the uneven point density of the canopy when the