Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR.

Harold F Murcia,Sebastian Tilaguy,Sofiane Ouazaa

doi:10.3390/plants10122804

Abstract

Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.

Highlights

Information on geometrical and structural characteristics of crops reveals important insight and provides decisive knowledge for management within an orchard
A vehicle navigates in the crop environment and accumulates the point clouds obtained by the Light Detection and Ranging (LiDAR) taking into account its location relative to an initial position
The LiDAR sensor for outdoor applications has about half of the value in the incidence on the total cost and the navigation unit ~30%, the most influential elements according to the authors of [30], who report the development of a low-cost system for 3d mapping in aerial platforms with a hypothetical selling price about 22,500 USD

Summary

Introduction

Information on geometrical and structural characteristics of crops reveals important insight and provides decisive knowledge for management within an orchard. Numerous techniques and sensors have been used to extract useful information efficiently and effectively and facilitate early detection and action tasks for farmers These technologies are expected to revolutionize agriculture, enabling timely decision-making, promising significant cost reduction, and increasing the crop yield [1]. Mobile crop mapping consists of digital modeling of the crop with a sufficiently dense point cloud to describe the morphological characteristics of the terrain and the plants This information can be used to assess the growth of the crop, estimate geometric tree characteristics, detect fruits, characterize a crop, estimate a crop yield, monitor the crop canopy, determine the crop biomass, leaf area index, and other high-throughput phenotyping parameters [3]. The challenge with low-cost systems involves the deployment of simple devices for 3D reconstruction, e.g., the absence of an accurate device with good resolution for the measurement of the position of the mobile on the trajectory and the use of two-dimensional LiDAR sensors, which offer only one plane of information

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Conclusion