On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

Abstract

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

“…These image-based methods are able to identify and match key points between overlapping images. In contrast to LiDAR-based solutions [27], the application of SfM enables collecting the fit-for-purpose data to model the geometry from some viewpoints. In general, plant modelling poses some challenges due to irregular surfaces, occlusion and varying illumination.…”

“…Then biomass information was used to assess its relationship with in situ LAI. Other studies, such as that by Moreno et al [27], used terrestrial LiDAR sensors to reconstructed vineyard crops. Although accurate, these methods are time-consuming and very expensive.…”

A Low-Cost 3D Phenotype Measurement Method of Leafy Vegetables Using Video Recordings from Smartphones

“…Wu et al [10] used it to collect point cloud data of corn plants and achieved high-precision phenotypic parameter estimation. Moreno et al [11] used on-board LiDAR to reconstruct vineyard crops and obtain vineyard biomass through data processing. In addition, Kinect is a universal and relatively cheap active sensor that can collect RGB-D data.…”

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map

“…Therefore, it is supposed that a reference 2D map of the environment obtained at a fixed height will be valid for a long period of time, reducing the need of creating new maps or updating already existing ones for the 2D mobile robot self-location procedure. This new proposal can be used to include SLAM in simple mobile robots using a fixed 2D push-broom LIDAR for obstacle avoidance, in transversal precision agriculture applications where a fixed 2D LIDAR is mainly used to gather information about plant architecture [ 17 , 18 , 19 ], and in combining SLAM and obstacle avoidance in mobile robots carrying a motorized 2D LIDAR.…”