Abstract:In order to obtain the external phenotypic parameters of the leaves and grasp the growth status of the plants quickly and efficiently, a threedimensional estimation method of leaf length, leaf width and leaf area was proposed based on a geometric model by using the leaves of money plant. The Microsoft Kinect V2 camera was used to obtain the local point cloud of the leaf from the 80cm height vertical pose and perform preprocessing such as passthrough filtering, denoising and simplification of the bounding box. The shape parameters of the point cloud were measured, and the preestablished SAE network classification prediction was used to obtain the geometric model parameters. The geometric model of the blade was established based on the surface parameter equation. The particle swarm optimization algorithm was used to calculate the spatial distance between the discrete point cloud and the local point cloud of the geometric model for spatial matching. The genetic algorithm was used to solve the internal model parameters of the optimal matching model, and the leaf length, leaf width and leaf area of the optimal matching model were output, were used as the estimation result. A total of 150 point cloud data were collected from the experiments. The estimated results and real values were analyzed by mathematical statistics and linear regression analysis. The average errors of the estimated leaf length, leaf width, and leaf area were 0.46cm and 0.41cm and 3.42 cm2, respectively. The R2 and RMSE of estimated leaf length were 0.88 and 0.52cm, the R2 and RMSE of leaf width were 0.88 and 0.52cm, and the R2 and RMSE of leaf area were 0.95 and 3.60cm2, respectively. It can be known from the experimental results that this method had good estimation effect on the shape parameters of money plant leaves, and it had high practical value.
