外文摘要：Remote sensing in agriculture aims to search new methods to monitor fruit at the tree, thus improving the estimation of yield-related variables. Light detection and range (LiDAR) scanning was introduced to obtain geometric and radiometric information from fruit surfaces by means of 3D point clouds. A geometric model to estimate apple size by means of segmented 3D point cloud of fruit is proposed in the present study. The model consists in the approximation of 3D point clouds to a reference shape given by 2D Fourier series expansion. Each point cloud was approximated to its reference shape using an iterative error minimization routine. The geometric model was applied to laboratory and field data of spheres and apples during fruit growth, ranging from 60 to 151 days after full bloom (DAFB). An overall $RMSE$RMSE between measured and predicted fruit radius of 20.1, 76.8, and 119.1% was found for the geometric model, mean, and maximum Euclidean distance approaches, respectively, including all studied growth periods in field conditions. Moreover, the linear regression on measured and predicted values showed considerably improved coefficient of determination (${R<^>2}$R2) of the geometric model in comparison to Euclidean distance calculations with ${R<^>2}$R2 values of 0.76 and 0.49 for laboratory and field scanned apples, respectively. The data processing method enables fruit monitoring and their application of terrestrial LiDAR sensing in precise orchard management.
外文关键词：LiDAR;apple;3D point clouds;centroid approximation;fourier series;ToF sensor
作者：Tapia-Zapata, Nicolas;Saha, Kowshik Kumar;Tsoulias, Nikos;Zude-Sasse, Manuela
作者单位：Leibniz Inst Agr Engn & Bioecon ATB
期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES
期刊影响因子：0.0
出版年份：2024
出版刊次：27（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台