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AI in Current and Future Agriculture: An Introductory Overview

KUNSTLICHE INTELLIGENZ 2023年第2-4期37卷 117-132页

作者： Kisliuk, Benjamin Krause, Jan Christoph Meemken, Hendrik Saborio Morales, Juan Carlos Mueller, Henning Hertzberg, Joachim DFKI Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Inst Comp Sci Osnabruck Germany

In recent years, agriculture has become a major field of application and transfer for AI. The paper gives an overview of the topic, focusing agricultural processes and technology in Central-European style arable farming. AI could also be part of the transformation process of agriculture that is emerging world-wide in response to the UN global sustainable development goals (SDGs). In that sense, our overview includes pointers to some research towards AI in future agricultural systems.

关键词： Agricultural technology Smart farming Agricultural robotics

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A systematic approach to the development of long-term autonomous robotic systems for agriculture 43

A systematic approach to the development of long-term autono...

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43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023

作者： Bohne, Tim Parthasarathy, Gurunatraj Kisliuk, Benjamin GmbH Plan-Based Robot Control Berghoffstraße 11 Osnabrueck49090 Germany

ISBN: (纸本)9783885797241

For robots to compete with conventional machinery in agriculture, improving their long-term autonomy seems necessary. In this article, we provide concepts and intermediate results of our work aimed at a long-term autonomous robotic system performing a plant monitoring task. based on notions from literature, we introduce a structured approach to defining long-term autonomy and report on the system we develop that meets this definition in practical experiments. Finally, we present intermediate results from simulation and discuss further avenues of research. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Agriculture

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RadaRays: Real-Time Simulation of Rotating FMCW Radar for Mobile robotics via Hardware-Accelerated Ray Tracing

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IEEE robotICS AND AUTOMATION LETTERS 2025年第3期10卷 2470-2477页

作者： Mock, Alexander Magnusson, Martin Hertzberg, Joachim Osnabruck Univ KBS Grp D-49074 Osnabruck Germany Orebro Univ RNP Lab AASS Res Ctr S-70182 Orebro Sweden DFKI Niedersachsen Plan based Robot Control Grp D-49084 Osnabruck Germany

RadaRays allows for the accurate modeling and simulation of rotating FMCW radar sensors in complex environments, including the simulation of reflection, refraction, and scattering of radar waves. Our software is able to handle large numbers of objects and materials in real-time, making it suitable for use in a variety of mobile robotics applications. We demonstrate the effectiveness of RadaRays through a series of experiments and show that it can more accurately reproduce the behavior of FMCW radar sensors in a variety of environments, compared to the ray casting-based lidar-like simulations that are commonly used in simulators for autonomous driving such as CARLA. Our experiments additionally serve as a valuable reference point for researchers to evaluate their own radar simulations. By using RadaRays, developers can significantly reduce the time and cost associated with prototyping and testing FMCW radar-based algorithms. We also provide a Gazebo plugin that makes our work accessible to the mobile robotics community.

关键词： Radar Radar imaging robots Spaceborne radar Ray tracing Meteorological radar Real-time systems Radar scattering Radar antennas Radar cross-sections Simulation and animation range sensing software tools for robot programming SLAM collision avoidance

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Object Anchoring for Autonomous robots Using the Spatio-Temporal-Semantic Environment Representation SEEREP 1

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46th German Conference on Artificial Intelligence (KI)

作者： Niemeyer, Mark Renz, Marian Hertzberg, Joachim German Res Ctr Artificial Intelligence Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany

ISBN: (数字)9783031426087

ISBN: (纸本)9783031426070;9783031426087

For single-plant specific weed regulation, robotic systems and agricultural machinery in general have to collect a large amount of temporal and spatial high-resolution sensor data. SEEREP, the Spatio-Temporal-Semantic Environment Representation, can be used to structure and manage such data more efficiently. SEEREP deals with the spatial, temporal and semantic modalities of data simultaneously and provides an efficient query interface for all three modalities that can be combined for high-level analyses. It supports popular robotic sensor data such as images and point clouds, as well as sensor and robot coordinate frames changing over time. This query interface enables high-level reasoning systems as well as other data analysis methods to handle partially unstructured environments that change over time, as for example agricultural environments. But the current methodology of SEEREP cannot store the result of the analysis methods regarding specific objects instances in the world. Especially the results of the anchoring problem which searches for a connection between symbolic and sub-symbolic data cannot be represented nor queried. Thus, we propose a further development of the SEEREP methodology in this paper: For a given object, we link the existing semantic labels in different datasets to a unique common instance, thereby enabling queries for datasets showing this object instance and with this enabling the efficient provision of datasets for object-centric analysis algorithms. Additionally, the results of those algorithms can be stored linked to the instance either by adding facts in a triple-store like manner or by adding further data linked to the instance, like a point, representing the position of the instance. We show the benefits of our anchoring approach in an agricultural setting with the use-case of single-plant specific weed regulation.

关键词： anchoring environment representation selective weeding

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Streamlined Acquisition of Large Sensor Data for Autonomous Mobile robots to Enable Efficient Creation and Analysis of Datasets

Streamlined Acquisition of Large Sensor Data for Autonomous ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Niemeyer, Mark Arkenau, Julian Puetz, Sebastian Hertzberg, Joachim German Res Ctr Artificial Intelligence DFKI Niedersachsen Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany Nat Robots GmbH Osnabruck Germany

ISBN: (纸本)9798350384581;9798350384574

The increasing usage of modern AI techniques represents a transforming shift in the robotics domain. Training and accessing new models requires substantial amounts of application-specific data, but the limited resources onboard mobile robots (like processing power, network bandwidth, etc.) pose a challenge for the development of efficient data recording and provisioning pipelines. Furthermore, accessing specific information based on a combination of spatial, temporal and semantic information is generally not supported by currently available tools. In this paper, we present a methodology which allows the efficient recording of robotic sensor data streams. We show that our approach reduces the overall time needed until the data can be served via the spatio-temporal-semantic query interface of the semantic environment representation SEEREP. We further present that the maximum sensor data rate which can be stored to disk in real-time is increased for large robotic data types like images and point clouds in comparison to frequently employed solutions within the ROS ecosystem.

关键词： Spatio-temporal data

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Scalable Evaluation Pipeline of CNN-based perception for robotic Sensor Data under different Environment Conditions 11

Scalable Evaluation Pipeline of CNN-based perception for Rob...

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11th European Conference on Mobile robots (ECMR)

作者： Iqbal, Naeem Niemeyer, Mark Krause, Jan Christoph Hertzberg, Joachim German Res Ctr Artificial Intelligence DFKI Niedersachsen Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany

ISBN: (纸本)9798350307047

Deep learning impacted a wide variety of perception applications for autonomous mobile robots. In classic computer vision benchmark tests, new algorithms keep appearing that outperform each other. However, these benchmark tests cannot be generalized, so that the specific application must be considered for the selection of sensors and algorithms. Especially in the agricultural domain, environmental conditions like weather and vegetation significantly influence the reliability of sensor systems. Therefore, it is essential to test different sensor modalities and algorithms in the operational design domain. This motivates the need for an evaluation framework which has the flexibility to compare and validate various perception algorithms, sensors suites, and data samples with a focus on different conditions. This paper proposes a pipeline combining a test environment (AI-TEST-FIELD), a semantic environment representation (SEEREP), and an inference server (Triton) for an automatic evaluation of different CNN-based perception algorithms under various environment conditions. Recurring and comparable recordings of raw sensor data with identical scenarios and objects can be performed on the test field, with the only difference being the environmental conditions. The inference results are inferred once and stored alongside the sensor data in SEEREP. Thus, they can be queried efficiently based on the environment conditions to generate (partially overlapping) subsets of the whole dataset. It is demonstrated how this pipeline can be used to apply the CNN-inference just once on the data, and how the queried subsets can subsequently be used to evaluate the performance in different environment conditions.

关键词： Pipelines

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Detecting spatio-temporal Relations by Combining a Semantic Map with a Stream Processing Engine

Detecting spatio-temporal Relations by Combining a Semantic ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nieeksch, Lennart Deeken, Henning Wiemann, Thomas German Res Ctr Artificial Intelligence DFKI DTKI Niedersachsen Plan Based Robot Control Grp Osnabruck Germany Osnabruck Univ Knowledge Based Syst & Autonomous Robot Grp Inst Comp Sci Osnabruck Germany

ISBN: (纸本)9798350323658

Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively researched, the reasoning about the temporal development of spatial relations of instances is often neglected. This paper presents a concept to combine a semantic map with a stream processing framework for live analysis of the spatio-temporal relation of objects, based on the map and information inferred from sensors streams. To demonstrate the functionality of our concept, we implemented a proof-of-concept system to track everyday events in an office environment. The presented application scenario clearly demonstrates the benefits of the proposed architecture for detecting and handling complex spatio-temporal events.

关键词： Semantics

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Automated Byproduct Segmentation In Grain Images 14

Automated Byproduct Segmentation In Grain Images

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14th International Conference on Machine Vision (ICMV)

作者： Herrmann, Lena Brust, Johannes Hertzberg, Joachim Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany DFKI Lab Niedersachsen Plan Based Robot Control Osnabruck Germany

ISBN: (数字)9781510650459

ISBN: (纸本)9781510650459;9781510650442

Within the farming industry using computer vision technologies provides great support for various processes. One example is the grain harvest with a combine harvester, which requires excellent expert knowledge to reach a certain level of quality. This means the amount of byproducts including straw and hulls should be as small as possible. Inside a combine harvester several sensors are installed which deliver important information about the current harvesting state. This includes a visual based sensor that constantly delivers images to the driver about the composition of the processed grain. Normally, it is the driver's task to decide if the quality is sufficient or if machine settings need to be adjusted to improve the outcome. Yet, resolving this task entirely manually is rather error prone, due to the high amount of fairly similar images that need to be analyzed in a short time. Therefore we designed and implemented a system that is able to automatically detect unwanted byproducts within the wheat harvest and highlight those parts inside images delivered by the visual sensor. The system itself is a combination of an automated preprocessing step and a variation of a U-Net. The preprocessing step filters unwanted byproducts and is able to automatically generate training data for the U-Net. The U-Net is lightweight, easy to train and can potentially be used onboard the combine harvester.

关键词： machine vision farming combine harvester image segmentation neural networks automated preprocessing

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Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs 17

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded System...

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7th IEEE International Conference on robotic Computing (IRC)

作者： Eisoldt, Marc Mock, Alexander Porrmann, Mario Wiemann, Thomas Osnabruck Univ Comp Engn Grp Osnabruck Germany Fulda Univ Appl Sci Robot Comp Sci Fulda Germany DFKI Nieder sachsen Plan Based Robot Control Osnabruck Germany

ISBN: (纸本)9798350395747

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in the literature that is able to solve it in real-time. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation.

关键词： Degrees of freedom (mechanics)

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MICP-L: Mesh-based ICP for robot Localization Using Hardware-Accelerated Ray Casting

MICP-L: Mesh-based ICP for Robot Localization Using Hardware...

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2024 International Conference on Intelligent robots and Systems

作者： Mock, Alexander Wiemann, Thomas Puetz, Sebastian Hertzberg, Joachim Osnabruck Univ Inst Comp Sci Knowledge Based Syst Grp Hamburger Str 24 D-49084 Osnabruck Germany Fulda Univ Appl Sci Robot Comp Sci Fulda Germany Nat Robots GmbH Osnabruck Germany DFKI Plan Based Robot Control Hamburger Str 24 D-49084 Osnabruck Germany

ISBN: (纸本)9798350377712;9798350377705

Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.

关键词： Mesh generation

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