A methodology for the realistic assessment of 3D point clouds of fruit trees in full 3D context

The aim of this paper is to address the lack of standard methodologies for the assessment of 3D point clouds. We present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points. Additionally, it introduces three significant innovations: a) it bridges the gap related to the unknown error of the reference ground-truth point cloud; b) it provides separate metrics for location error and reconstruction error; and c) it introduces a procedure to compute the location error that eliminates the bias in the selection of point-pair picking between the DGT points and their corresponding pairs in the point cloud being assessed.

The geometry and structure of trees are related to the vegetative parameters and productivity in fruit orchards. In consequence, obtaining a precise and accurate geometric characterization of canopies is of interest for implementing site-specific management strategies that optimize input rates and minimize the costs and environmental risks of agricultural operations. Among the different sensing technologies, sensors based on the principle of light detection and ranging (LiDAR) have emerged as the primary choice for accurate geometric characterization of orchards. However, to make informed orchard management decisions based on LiDAR-derived geometric and structural data, it is essential to assess the accuracy of LiDAR-based scanning systems. Unfortunately, there is currently a lack of standard methodologies to evaluate the accuracy of LiDAR-based systems in agricultural environments. This research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context. The methodology involves comparing LiDAR-derived point clouds to an accurate high-resolution 3D digital ground truth (DGT) obtained using digital photogrammetric techniques. One of the main difficulties when using a reference point cloud to assess point cloud errors is the selection of the points to be compared so that they can be considered as corresponding point pairs. When developing the methodology, four procedures of point pair selection and distance calculation were compared. The best performing procedure was selected and proposed as a standard for accuracy assessment of 3D point clouds. The proposed procedure minimizes the error attributed to the selection of the corresponding point pairs between the assessed point cloud and the reference DGT point cloud. Subsequently, the proposed methodology was tested and validated by assessing the accuracy of 46 different point clouds.

The conclusions regarding the accuracy, applicability, and practical utility of the proposed methodology are supported by the determination of reconstruction errors and location errors in 46 point clouds obtained with the 3 different MTLS systems operated with different settings. The proposed methodology will be very useful for scanning system manufacturers, researchers, advisors and eventually advanced farmers to quantify the errors committed when characterizing tree canopies. This is crucial to enable accurate management operations in the framework of Precision Agriculture based on canopy variability. Furthermore, the methodology is expected to facilitate the design of new applications requiring high accuracy to be implemented in the near future.