A Method for Predicting Canopy Light Distribution in Cherry Trees Based on Fused Point Cloud Data

Abstract

A proper canopy light distribution in fruit trees can improve photosynthetic efficiency, which is important for improving fruit yield and quality. Traditional methods of measuring light intensity in the canopy of fruit trees are time consuming, labor intensive and error prone. Therefore, a method for predicting canopy light distribution in cherry trees was proposed based on a three-dimensional (3D) cherry tree canopy point cloud model fused by multiple sources. First, to quickly and accurately reconstruct the 3D cherry tree point cloud model, we propose a global cherry tree alignment method based on a binocular depth camera vision system. For the point cloud data acquired by the two cameras, a RANSAC-based orb calibration method is used to externally calibrate the cameras, and the point cloud is coarsely aligned using the pose transformation matrix between the cameras. For the point cloud data collected at different stations, a coarse point cloud alignment method based on intrinsic shape signature (ISS) key points is proposed. In addition, an improved iterative closest point (ICP) algorithm based on bidirectional KD-tree is proposed to precisely align the coarse-aligned cherry tree point cloud data to achieve point cloud data fusion and obtain a complete 3D cherry tree point cloud model. Finally, to reveal the pattern between the fruit tree canopy structure and the light distribution, a GBRT-based model for predicting the cherry tree canopy light distribution is proposed based on the established 3D cherry tree point cloud model, which takes the relative projected area features, relative surface area and relative volume characteristics of the minimum bounding box of the point cloud model as inputs and the relative light intensity as output. The experiment results show that the GBRT-based model for predicting the cherry tree canopy illumination distribution has good feasibility. The coefficient of determination between the predicted value and the actual value is 0.932, and the MAPE is 0.116, and the model can provide technical support for scientific and reasonable cherry tree pruning.