We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.
SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping
Graduate School of Agricultural and Life Sciences, The University of Tokyo（东京大学大学院农学生命科学研究科）；National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学多谱信息智能处理技术全国重点实验室、人工智能与自动化学院）；Institute of Agricultural Machinery, NARO（日本国立研究开发法人农业·食品产业技术综合研究机构农业机械研究所）；Institute of Life and Environmental Sciences, University of Tsukuba（筑波大学生命环境科学研究所）
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Center for Robotics, University of Bonn（柏林洪堡大学机器人中心）；Institute of Sugar Beet Research（甘蔗研究院）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr人工智能与机器学习研究所）
In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we notice a similarity between the current state-of-the-art for separating corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a number of novel, application-specific algorithms with the goal of producing …
Tampere University（坦佩雷大学）；Sampoerna University（桑波尔纳大学）
The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This paper discusses the application of UAVs in agriculture, particularly in spraying and crop monitoring. This study examines the urgency of UAV implementation i…
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs（农业农村部光谱传感重点实验室）；College of Agriculture and Biotechnology, Zhejiang University（浙江大学农业与生物技术学院）
University of Delaware（特拉华大学）；TRIC Robotics LLC.（TRIC机器人有限责任公司）
Unmanned ground vehicles can capture a sub-canopy perspective for plant phenotyping, but their design and construction can be a challenge for scientists unfamiliar with robotics. Here we describe the necessary components and provide guidelines for designing and constructing an autonomous ground robot that can be used for plant phenotyping.
University of Western Ontario（西安大略大学）；Canadian Surgical Technologies & Advanced Robotics（加拿大外科技术与先进机器人中心）；Stryker Mako Surgical Corporation（史赛克马科外科公司）；Bulgarian Academy of Sciences（保加利亚科学院）；University of Guelph（圭尔夫大学）
Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple vi…
A Ground Mobile Robot for Autonomous Terrestrial Laser Scanning-Based Field Phenotyping
School of Electrical and Computer Engineering（电气与计算机工程学院）；The University of Georgia（佐治亚大学）；Bio-Sensing, Automation and Intelligence Laboratory（生物传感、自动化与智能实验室）；Agricultural & Biological Engineering（农业与生物工程）；The University of Florida（佛罗里达大学）
A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond
Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密歇根州立大学）；Department of Systems and Information Engineering & Link Lab, University of Virginia（系统与信息工程系及Link实验室，弗吉尼亚大学）；Department of Statistics and Probability, Michigan State University（统计与概率系，密歇根州立大学）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（化学、化工与生物技术学院，南洋理工大学）；Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密西西比州立大学）；Department of Mechanical Engineering, Michigan State University（机械工程系，密歇根州立大学）
We present PATHoBot an autonomous crop surveying and intervention robot for glasshouse environments. The aim of this platform is to autonomously gather high quality data and also estimate key phenotypic parameters. To achieve this we retro-fit an off-the-shelf pipe-rail trolley with an array of multi-modal cameras, navigation sensors and a robotic arm for close surveying tasks and intervention. In this paper we describe PATHoBot design choices made to ensure proper operation in a commercial glasshouse environment. As a surveying platform we collect a number of datasets which include both sweet…
School of Electric Engineering and Informatics Institut Teknologi Bandung（万隆理工学院电气工程与信息学院）
This research will proposed a new kind of relatively low cost autonomous UAV that will enable farmers to make just in time mosaics of aerial photo of their crop. These mosaics of aerial photo should be able to be produced with relatively low cost and within the 24 hours of acquisition constraint. The autonomous UAV will be equipped with payload management system specifically developed for rapid aerial mapping. As mentioned before turn around time is the key factor, so accuracy is not the main focus (not orthorectified aerial mapping). This system will also be equipped with special software to …
A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices rely on exhaustive coverage and data collection from the entire crop field being monitored under the breeding experiment. This works well in relatively small agricultural fields but can not be scaled to the larger ones, thus limiting the progress of genetics research. In this work…
Sapienza University of Rome（罗马第一大学）；National Research Council（国家研究委员会）；University of Bonn（波恩大学）；University of Milano-Bicocca（米兰比可卡大学）；University of Essex（埃塞克斯大学）；Technology Innovation Institute（技术创新研究所）；Princeton University（普林斯顿大学）
Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and ight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Iowa State University（爱荷华州立大学）
Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steer…
Sapienza University of Rome（罗马大学）；IT+Robotics Srl（IT+Robotics有限责任公司）；International Research Lab 2958 Georgia Tech-CNRS（佐治亚理工-法国国家科学研究中心2958国际研究实验室）；University of Freiburg（弗莱堡大学）；Toyota Research Institute（丰田研究院）；University of Bonn（波恩大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；Agency for Agro-food Sector Services of the Marche Region (ASSAM)（马尔凯大区农业食品部门服务局）；ETH Zurich（苏黎世联邦理工学院）
The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of …
Trường Đại học Công nghệ, Đại học Quốc gia Hà Nội（河内国家大学所属技术大学）；Trường Đại học Kinh tế - Kỹ thuật Công nghiệp（工业经济与技术大学）
Agricultural production using high technology is an inevitable trend in Vietnam. Especially for material crops which typically need large growing areas, wireless sensor networks has been clearly playing a significant role in increasing productivity, monitoring pests and diseases, mitigating the impact of climate change, and reducing the direct labor of cultivators. This paper constructs an experimental model of agricultural crop field monitoring using a combination of LoRa wireless sensor networks and unmanned aerial vehicles to collect data on conditions of weather and soil, plant health, whi…
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field
College of Engineering, South China Agriculture University（华南农业大学工程学院）；Department of Mechanical and Aerospace Engineering, Monash University（莫纳什大学机械与航空航天工程系）
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Dept. of Computer Science & Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Dept. of Mechanical Engineering, University of Minnesota（机械工程系，明尼苏达大学）
Sapienza University of Rome（罗马第一大学）；ETH Zurich（苏黎世联邦理工学院）
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within ag…
UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Pittsburgh（匹兹堡大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Boston University（波士顿大学）；Peking University（北京大学）
We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in hi…
Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms
Autonomous Robotics Systems Limited（自主机器人系统有限公司）；University of Hyderabad（海得拉巴大学）；ThoughtGreen Technologies Private Limited（ThoughtGreen科技私人有限公司）；Ideabytes Software India Private Limited（Ideabytes软件印度私人有限公司）；Georgia Institute of Technology（佐治亚理工学院）；Tata Consultancy Services（塔塔咨询服务公司）；EPAM India（埃培智印度分公司）；Kaveri University（卡弗里大学）；University of Malaya（马来亚大学）
Iterative Motion Compensation for Canonical 3D Reconstruction from UAV Plant Images Captured in Windy Conditions
Autonomous Intelligent Systems - Computer Science Institute VI and Center for Robotics, University of Bonn, Germany（自主智能系统 - 计算科学研究所VI和机器人中心，波恩大学，德国）；Lamarr Institute for Machine Learning and Artificial Intelligence, Germany（拉玛尔机器学习与人工智能研究所，德国）
Altitude-Adaptive Vision-Only Geo-Localization for UAVs in GPS-Denied Environments
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航天工程学院）；School of Instrumentation Science and Opto-electronics Engineering, Beijing Information Science and Technology University（北京信息科技大学仪器科学与光电工程学院）
An Aerial Manipulator for Perception-Driven Flower Targeting Toward Contactless Pollination in Vertical Farming
Department of Computer Science University College London London United Kingdom（计算机科学系伦敦大学学院伦敦英国）；Department of Artificial Intelligence University of Malaya Kuala Lumpur Malaysia（人工智能系马来大学吉隆坡马来西亚）；University College London（伦敦大学学院）；University of Malaya（马来大学）
Leveraging 5G private networks, UAVs and robots to detect and combat broad-leaved dock (Rumex obtusifolius) in feed production
Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵兰-普法尔茨州凯泽斯劳滕-兰道工业大学）
