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

José María Bengochea-Guevara,Dionisio Andújar,Hugo Moreno,Constantino Valero,Miguel Garrido-Izard,Ángela Ribeiro

doi:10.3390/s20041102

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.

Highlights

Nowadays, agriculture features much more in terms of economy than it did in the past, since food drives the world where access to adequate food is the primary concern
The extracted volume values showed that the 3D models were able to properly estimate vine pruning weight in a fast and reliable manner
Three-dimensional (3D) models were possible, since the 2D light detection and ranging (LiDAR) divide was displaced along the rows to obtain a 3D structure of the crop

Summary

Introduction

Agriculture features much more in terms of economy than it did in the past, since food drives the world where access to adequate food is the primary concern. A major ongoing concern in the preservation of the environment leads to a more sustainable agriculture At this critical juncture, Precision Agriculture (PA) has to deal with the current and future agriculture market as it increasingly demands improved productivity and efficiency together with the goals of saving inputs, higher yields, cutting costs, saving time, and making it more profitable [2]. Farming management strategies and a site-specific treatment are needed to help farmers on the grounds of decision-making policies to enhance production while being sustainable [3]. In this scenario, a detailed characterization of plant structure leads to an optimum management of crops. A thorough knowledge of plant architecture allows an adequate application of pesticides, fertilizers, and water inputs; it helps to reduce costs and to obtain a higher crop yield

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