Low-Cost Three-Dimensional Modeling of Crop Plants

Abstract

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.

Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration

“…According to the Graham algorithm [41], the set of convex points on the XOZ plane was searched, as shown in Figure 8b, to calculate the W of the convex point set as the W of the canopy. The calculation equation is shown in Equation (11), and the connecting line for the W is shown in Figure 8b. Based on multiview RGB-D 3D reconstruction, the change of the initial reference point cloud would affect the projection morphology of the canopy point cloud on the XOY and YOZ planes, but the projection of the tomato canopy on the XOZ horizontal plane was not affected by the initial point cloud view.…”

“…To verify the applicability of the multiview RGB-D reconstruction method and the morphological feature parameter calculation method proposed in this study, the 3D point cloud reconstruction was performed on 60 GTPs, obtaining the RGB point cloud maps of the tomato plants The correlation between the actual measurement values of the plant canopy H and W and the Kinect measurement values is shown in Figure 13a,b. The Kinect measurements of the plant canopy H and W could be calculated directly from the reconstructed point cloud, as shown in Equations (10) and (11). As shown in Table 4, in the V3 measurement mode, the MIN, MAX, and AVG of the R 2 between the Kinect measurement value and the manual measurement value of the canopy H of the plants were 0.9883, 0.9897, and 0.9890, respectively; the MIN, MAX, and AVG of the RMSE were 0.30, 10.88, and 3.15 cm, respectively; and the MIN, MAX, and AVG of the RAD were 0.41%, 18.05%, and 5.53%, respectively.…”

“…The correlation between the actual measurement values of the plant canopy H and W and the Kinect measurement values is shown in Figure 13a,b. The Kinect measurements of the plant canopy H and W could be calculated directly from the reconstructed point cloud, as shown in Equations (10) and (11). The correlation between the actual measurement values of the plant canopy H and W and the Kinect measurement values is shown in Figure 13a,b.…”

“…Thus, monocular vision has relatively low applicability. Multiview reconstruction techniques based on monocular vision mainly include space carving [9], visual structure from motion system techniques [10,11], multiview photogrammetry [12,13], and multicamera synchronous reconstruction [14]. However, these techniques require many angles of view (AOVs) for measurement and are unable to meet the reconstruction efficiency requirement for high-throughput plant phenotyping.…”

A Rapid Method of the Rock Mass Surface Reconstruction for Surface Deformation Detection at Close Range

“…Recent developments in 3D photogrammetry could create 3D surface models or triangulated irregular networks (TINs) using multiple photos [ 12 , 13 ]. Owing to the economy, convenience, and intelligence, the structure from motion–multi view stereo (SfM-MVS) has attracted continuous investigations into acquiring digital information within 3D reconstruction [ 14 , 15 ]. The SfM calculates the object’s position based on the reference point deduced from photos, which is mainly used for sparse reconstruction.…”