基于三维点云的群体樱桃树冠层光照分布预测模型

合理的果树冠层结构和栽培密度可提高其冠层内光截获量，对提升果实产量和质量有重要影响。本文以细纺锤形樱桃树为研究对象，构建了基于三维点云的群体樱桃树冠层光照分布预测模型。使用Azure Kinect DK相机获取群体樱桃树三维点云数据，通过点云数据预处理得到完整的群体樱桃树三维点云数据。在冠层尺度内，对樱桃树冠层点云数据进行分层，提取不同区域的点云颜色特征。提出基于Delaunay三角化凹包算法的点云投影面积计算方法，通过凹包边界点提取和向量积叉乘，计算不同区域的点云投影面积。以点云颜色特征和相对投影面积特征为输入，以实测相对光照强度为输出，建立群体樱桃树冠层光照分布预测模型。试验结果表明，该模型能够较为准确地预测樱桃树冠层内的光照分布，预测值与实际值决定系数平均值为0.885，均方根误差为0.0716。研究结果可为樱桃树合理的种植密度管理及樱桃树休眠期自动化剪枝等提供技术支持。

Light Distribution Prediction Model of Group Cherry Trees Canopy Based on 3D Point Cloud

A reasonable canopy structure and cultivation density of fruit trees can increase the amount of light interception within their canopies, which has an important impact on improving the yield and quality of fruit. A 3D point cloud based model for predicting the canopy light distribution of group cherry trees was proposed by using a thin spindle shaped cherry tree as the research object. Firstly, the Azure Kinect DK camera was used to obtain the 3D point cloud data of the group cherry trees, and the complete 3D point cloud data of the group cherry trees were obtained through point cloud data pre processing. Secondly, according to the actual cherry tree canopy segmentation method, the point cloud data of cherry tree canopies were point cloud stratified and the point cloud colour features of different regions were extracted. Again, a point cloud projection area calculation method based on the Delaunay triangulated concave packet algorithm was proposed to calculate the point cloud projection area of different regions through concave packet boundary point extraction and vector product fork multiplication. Finally, a model for predicting the light distribution in the canopy of group cherry trees was developed, which was a random forest model with point cloud colour characteristics and relative projected area characteristics as input and measured relative light intensity as output. The experimental results showed that the model was able to predict the light distribution in the canopy of cherry trees with a mean coefficient of determination of 0.885 and root mean square error of 0.0716. The research results can provide technical support for reasonable planting density management and automated pruning of cherry trees during dormancy.