Three-dimensional reconstruction of the furrow shape in orchards using a low-cost lidar - Zhou, Xinzhao; Wu, Yanfeng; Meng, Hewei; Han, Shujie; Kan, Za; Li, Yaping; Zhang, Jie (Frontiers in Sustainable Food Systems, 2023)
Efficient furrow fertilization is extremely critical for fertilizer utilization, fruit yield, and fruit quality. The precise determination of trench quality necessitates the accurate measurement of its characteristic parameters, including its shape and three-dimensional structure. Some existing algorithms are limited to detecting only the furrow depth while precluding the tridimensional reconstruction of the trench shape. In this study, a novel method was proposed for three-dimensional trench shape reconstruction and its parameter detection. Initially, a low-cost multi-source data acquisition system with the 3D data construction method of the trench was developed to address the shortcomings of single-sensor and manual measurement methods in trench reconstruction. Subsequently, the analysis of the original point cloud clarified the “coarse-fine” two-stage point cloud filtering process, and then a point cloud preprocessing method was proposed based on ROI region extraction and discrete point filtering. Furthermore, by analyzing the characteristics of the point cloud, a random point preselection condition based on the variance threshold was designed to optimize the extraction method of furrow side ground based on RANSAC. Finally, a method was established for extracting key characteristic parameters of the trench and trench reconstruction based on the fitted ground model of the trench side. Experimental results demonstrated that the point cloud pretreatment method could eliminate 83.8% of invalid point clouds and reduce the influence of noise points on the reconstruction accuracy. Compared with the adverse phenomena of fitting ground incline and height deviation of the original algorithm results, the ground height fitted by the improved ditch surface extraction algorithm was closer to the real ground, and the identification accuracy of inner points of the ground point cloud was higher than that of the former. The error range, mean value error, standard deviation error, and stability coefficient error of the calculated ditch width were 0 ~ 5.965%, 0.002 m, 0.011 m, and 0.37%, respectively. The above parameters of the calculated depth were 0 ~ 4.54%, 0.003 m, 0.017 m, and 0.47%, respectively. The results of this research can provide support for the comprehensive evaluation of the quality of the ditching operation, the optimization of the structure of the soil touching part, and the real-time control of operation parameters.