Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

Nguyen, Phat T.; Badenhorst, Pieter; Shi, Fan; Spangenberg, Germán; Smith, K. F.; Daetwyler, Hans D.

doi:10.3390/rs13010020

Cited by 15 publications

(15 citation statements)

References 81 publications

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“…The difference in rolling resistance or traction in the wheels of a differential drive robot can make the robot turn unexpectedly, making the robot less precise in steering control. Some wheeled robots use two wheels for steering and two wheels for driving (2WD2WS) [31,34]. The locomotion is controlled by the forward/backward speed and turning angle, similar to an Ackermann steering, enabling precise steering control.…”

Section: Mobile Platformmentioning

confidence: 99%

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A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

2022

Plant Phenomics

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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

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Section: Mobile Platformmentioning

confidence: 99%

“…Plant height and row spacing estimation [58] DairyBioBot [34] RTK-GNSS Ryegrass Ryegrass biomass estimation [34] 7 Plant Phenomics Development of row-following algorithm in polytunnels [60] Ladybird [37] RGB camera2D LiDAR Hyperspectral camera…”

Section: Cornmentioning

confidence: 99%

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

2022

Plant Phenomics

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“…Over the years UGVs (Unmanned Ground Vehicles), also known as UTVs (Unmanned Terrestrial Vehicles) or USVs (Unmanned Surface Vehicles) have been developed and used in many fields for several different applications, ranging from the automation of farm tasks and the precision agriculture field (Bechtis et al, 2017) to mine inspection (Szrek et al, 2020). Moreover, mobile terrestrial systems are recently reported to be increasingly used for agricultural science applications (Nguyen et al, 2020). Moreover, mobile robots have been exploited for search and rescue (SAR) operations in various dangerous environments, to enhance efficiency but also to improve the safety of the rescue personnel.…”

Section: Introductionmentioning

confidence: 99%

UAV-UGV imagery for disaster scenario mapping: aerial-terrestrial data fusion and comparison

Potente¹,

Cagnazzo²,

Vött³

et al. 2021

ROL

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This paper introduces a mobile robotic system equipped with RBG (Red, Green, Blue) and IRT (InfraRed Thermography) sensors, designed in a joint industrial research project, which aims to develop low-cost technologies for the survey of dangerous scenarios in remote mode. The methodology adopted for mapping a landslide scenario, based on aerial and terrestrial unmanned systems, is presented. The UAV (Unmanned Aerial Vehicle) has been employed to map the damaged buildings and the landslide slope, by planning ten survey missions; the UGV (Unmanned Ground Vehicle) has been employed as a complementary mobile photogrammetry technique to obtain highresolution ground data, by planning three missions, each consisting of two passes along the same paths simulating a two-camera setting. UAV and UGV-derived datasets have been processed using the Structure-from-Motion technique in order to obtain photogrammetry products such as ortho-photomosaics, digital terrain models, point clouds and 3D models of the building. Aerial and terrestrial models have been integrated, using GCPs (ground control points) coordinates and natural/artificial control points, and have been exploited for the spatial analysis.An accuracy assessment has been carried out in CloudCompare using the Cloud-to-Cloud function and local modelling: the aerial and terrestrial models of buildings facades have been independently compared with a reference model, obtained from a stationary photogrammetry survey. The results highlight the higher reliability of the terrestrial model for accurate surfaces reconstruction, proving the potential of terrestrial mobile photogrammetry as an effective lowcost mapping solution for hazardous environments. Moreover, UGV mission settings, datasets characteristics and potential improvements are discussed in order to portray the benefits and limitations of this approach.

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“…This concept has been addressed in the field of robotics for many years under the concept of Simultaneous Localization and Mapping (SLAM) [15]. Consulted applications use the generated point clouds for fruit detection, yield prediction [14], canopy characterization [16], automatic panicles detection [17], high-throughput phenotyping [5,18], low cost phenotyping [16], and fast phenotyping [19], among others. Table 1 summarizes the applications, crops, and used sensors on recent works.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 summarizes the applications, crops, and used sensors on recent works. High-throughput phenotyping Maize 3D Velodyne HDL64-S3 GNSS receiver with two antennas UGV [5] Ryegrass 2D SICK LMS-400 Here+ V2 RTK GPS UGV [18] The development of a high-resolution phenotyping platform integrated with postprocessing technology offers the opportunity to assess complex traits more effectively. Compared to other platform types, UGVs reduce personnel time to cover large test plots and provide more accurate measurement because they can operate at constant speed and avoid human error.…”

Section: Introductionmentioning

confidence: 99%

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

Murcía

Tilaguy

Ouazaa

2021

Plants

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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.

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Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

Cited by 15 publications

References 81 publications

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

UAV-UGV imagery for disaster scenario mapping: aerial-terrestrial data fusion and comparison

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

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