Building an Aerial–Ground Robotics System for Precision Farming: An Adaptable Solution

Pretto, Alberto; Aravecchia, Stéphanie; Burgard, Wolfram; Chebrolu, Nived; Dornhege, Christian; Falck, Tillmann; Fleckenstein, Freya; Fontenla, Alessandra; Imperoli, Marco; Khanna, Raghav; Liebisch, Frank; Lottes, Philipp; Milioto, Andres; Nardi, Daniele; Nardi, S.; Pfeifer, Johannes; Popović, Marija; Potena, Ciro; Pradalier, Cédric; Rothacker-Feder, Elisa; Sa, Inkyu; Schaefer, Alexander; Siegwart, Roland; Stachniss, Cyrill; Walter, Achim; Winterhalter, Wera; Wu, Xiaolong; Nieto, Juan

doi:10.1109/mra.2020.3012492

Cited by 78 publications

(45 citation statements)

References 33 publications

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“…Traditionally, a lot of the activities required substantial manual work. Over the last decade, however, automated plant phenotyping has been receiving increasing interest, also in other disciplines such as robotics [10,11] or PLOS ONE computer vision [12][13][14]. One relevant aspect in this context relates to obtaining relevant features of plants, often referred to as phenotypic traits, in an automated manner [2,15].…”

Section: Related Workmentioning

confidence: 99%

Registration of spatio-temporal point clouds of plants for phenotyping

et al. 2021

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Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.

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Section: Related Workmentioning

confidence: 99%

Registration of spatio-temporal point clouds of plants for phenotyping

et al. 2021

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“…Using appropriate sensors, unmanned aerial systems (UAS) are increasingly used for carrying out such operations and determining phenotype characteristics (Roth et al, 2018). Pretto et al (2019) developed an exciting solution for improving precision-management of crop stands through coupling UAS with unmanned ground vehicles (UGV). UGS infer spatial information about crop density, nitrogen status and weed infestation from processing signals of appropriate sensors directed towards large areas, transmit this information into UGVs for comparison against their own perceptions of plant stands and environments, negotiate both types of information through dedicated algorithms and decide on automated management actions on this basis.…”

Section: Remote Sensing Artificial Intelligence and Roboticsmentioning

confidence: 99%

Linking integrative plant physiology with agronomy to sustain future plant production

Langensiepen

Jansen

Wingler

et al. 2020

Environmental and Experimental Botany

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Sustainable production of high-quality food is one of today's major challenges of agriculture. To achieve this goal, a better understanding of plant physiological processes and a more integrated approach with respect to current agronomical practices are needed. In this review, various examples of cooperation between integrative plant physiology and agronomy are discussed, and this demonstrates the complexity of these interrelations. The examples are meant to stimulate discussions on how both research areas can deliver solutions to avoid looming food crises due to population growth and climate change. In the last decades, unprecedented progress has been made in the understanding of how plants grow and develop in a variety of environments and in response to biotic stresses, but appropriate management and interpretation of the resulting complex datasets remains challenging. After providing an historical overview of integrative plant physiology, we discuss possible avenues of integration, involving advances in integrative plant physiology, to sustain plant production in the current post-omics era. Finally, recommendations are provided on how to practice the transdisciplinary mindset required, emphasising a broader approach to sustainable production of high-quality food in the future, whereby all those who are involved are made partners in knowledge generation processes through transdisciplinary cooperation.

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“…In recent years, the use of unmanned aerial vehicles (UAVs) has increased significantly in various fields, including surveillance, agriculture, transportation, rescue and military [1][2][3][4][5]. Accordingly, for safe UAV navigation that avoids collisions, research on perception of flight environments such as object detection, tracking and mapping are actively conducted [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

Lee

Kim

2021

Drones

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For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of the UAV needs to recognize information such as the position and velocity of all objects in the surrounding local area regardless of the type of object. At the same time, a probability based representation taking into account the noise of the sensor is also essential. In addition, a software design with efficient memory usage and operation time is required in consideration of the hardware limitations of the UAVs. In this paper, we propose a 3D Local Dynamic Map (LDM) generation algorithm for a perception system for UAVs. The proposed LDM uses a circular buffer as a data structure to ensure low memory usage and fast operation speed. A probability based occupancy map is created using sensor data and the position and velocity of each object are calculated through clustering between grid voxels using the occupancy map and velocity estimation based on a particle filter. The objects are predicted using the position and velocity of each object and this is reflected in the occupancy map. This process is continuously repeated and the flying environment of the UAV can be expressed in a three-dimensional grid map and the state of each object. For the evaluation of the proposed LDM, we constructed simulation environments and the UAV for outdoor flying. As an evaluation factor, the occupancy grid is accuracy evaluated and the ground truth velocity and the estimated velocity are compared.

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Building an Aerial–Ground Robotics System for Precision Farming: An Adaptable Solution

Cited by 78 publications

References 33 publications

Registration of spatio-temporal point clouds of plants for phenotyping

Registration of spatio-temporal point clouds of plants for phenotyping

Linking integrative plant physiology with agronomy to sustain future plant production

An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

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