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6th Nordic e-Infrastructure Collaboration Conference, NeIC 2024

作者： Baker, Dirk Norbert Bauer, Felix Schnepf, Andrea Scharr, Hanno Riedel, Morris Göbbert, Jens Henrik Hvannberg, Ebba School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany Institute of Bio- and Geosciences 3: Agrosphere Forschungszentrum Jülich GmbH Jülich Germany Institute for Advanced Simulation 8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany

ISBN: (纸本)9783031862397

E-infrastructures deliver basic supercomputing and storage capabilities but can benefit from innovative higher-level services that enable use-cases in critical domains, such as environmental and agricultural science. This work describes methods to distribute virtual scenes to the GPU nodes of a modular supercomputer for data generation. High information density virtual scenes, containing >100k geometries, typically cannot be rendered in real-time without techniques that change the information content, such as level-of-detail or culling approaches. Our work enables the concurrent and partitioned coupling to the image analysis in such a way that the data generation is dynamic and can be allocated to GPU nodes on demand, resulting in the possibility of moving through a continuous virtual scene rendered on multiple nodes. Within agricultural data analysis, the approach is especially impactful as virtual fields contain many individual geometries that coexist in one continuous system. Our work facilitates the generation of high-quality image data sets, which has the potential to solve the challenge of scarcity of well-annotated data in agricultural science. We use real-time communication standards to couple the data production with the image analysis training. We demonstrate how the use-case rendering impacts effective use of the compute nodes and furthermore develop techniques to distribute the workload to improve the data production. © The Author(s) 2025.

关键词： Virtual environments

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VRoot: A VR-Based application for manual root system architecture reconstruction

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Plant Phenomics 2025年第2期7卷

作者： Baker, Dirk N. Selzner, Tobias Göbbert, Jens Henrik Scharr, Hanno Riedel, Morris Hvannberg, Ebba þóra Schnepf, Andrea Zielasko, Daniel Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany School of Engineering and Natural Sciences University of Iceland Reykjavík Iceland Institute of Bio- and Geosciences 3: Agrosphere Forschungszentrum Jülich GmbH Jülich Germany Institute of Advanced Simulation 8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany Human-Computer Interaction Trier University Trier Germany

This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns. In practical scenarios, experimental conditions will be adapted to fit the need of the data analysis pipeline, including sieving and drying the soil before scanning. Based on previous reports of automatic systems that do not represent what experts would annotate, we developed a virtual reality system to assist with the extraction of root systems in cases in which automated approaches fall short of expert knowledge. The aim of the present study is to evaluate whether our immersive method is superior to classical annotation approaches when tested on synthetic data sets using untrained participants. Our laboratory user study consists of evaluating the root extractions of participants, along with their rating on central user experience and usability measures. We show significant improvement in F1 score across conditions (noisy or clear data) as well as an improved usability. Our study highlights that using virtual reality in root extraction improves accuracy, and we perform an in-depth evaluation of biases that occur when users trace roots in soil volumes. © 2025

关键词： Photointerpretation

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Retrieval of sun-induced plant fluorescence in the O2-A absorption band from DESIS imagery

arXiv

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arXiv 2024年

作者： Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Müller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno Forschungszentrum Jülich GmbH Institute of Bio- and Geosciences IBG-2: Plant Sciences Jülich Germany Oberpfaffenhofen Germany Largo Galileo Galilei Frascati00044 Italy Forschungszentrum Jülich GmbH Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Jülich Germany

We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation (r2 = 0.6) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA’s upcoming FLEX mission we develop a method for SIF retrieval in the O2-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW nm−1 sr−1 m−2 © 2024, CC BY.

关键词： Physiological models

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Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

arXiv

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arXiv 2024年

作者： Seiffarth, Johannes Blöbaum, Luisa Paul, Richard D. Friederich, Nils Yamachui Sitcheu, Angelo Jovin Mikut, Ralf Scharr, Hanno Grünberger, Alexander Nöh, Katharina Institute of Bio- and Geosciences IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany Multiscale Bioengineering Bielefeld University Bielefeld Germany Institute for Advanced Simulation IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Microsystems in Bioprocess Engineering Karlsruhe Institute of Technology Karlsruhe Germany

Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking performance are not yet known. Therefore, we present the largest publicly available and annotated dataset for MLCI, containing more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. With this dataset at hand, we generalize existing tracking metrics to incorporate relevant imaging and experiment parameters into experiment-aware metrics. These metrics reveal that current cell tracking methods crucially depend on the choice of the experiment parameters, where their performance deteriorates at high imaging intervals and large cell colonies. Thus, our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods that generalize across imaging and experiment parameters. The benchmark dataset is publicly available at https://***/doi/10.5281/zenodo.7260136. © 2024, CC BY.

关键词： Cytology

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Deep learning Based Prediction of Sun-Induced Fluorescence from Hyplant Imagery

Deep Learning Based Prediction of Sun-Induced Fluorescence f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jim Buffat Miguel Pato Kevin Alonso Stefan Auer Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Uwe Rascher Hanno Scharr Forschungszentrum Jülich GmbH Institute of Bio- and Geosciences IBG-2: Plant Sciences Jülich Germany German Aerospace Center (DLR) Earth Observation Center Remote Sensing Technology Institute Oberpfaffenhofen Germany RHEA Group c/o European Space Agency (ESA) Frascati Italy Forschungszentrum Jülich GmbH Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Jülich Germany

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

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Fast machine learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant

Fast Machine Learning Simulator of At-Sensor Radiances for S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Miguel Pato Kevin Alonso Stefan Auer Jim Buffat Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Uwe Rascher Hanno Scharr Earth Observation Center Remote Sensing Technology Institute German Aerospace Center (DLR) Germany Largo Galileo Galilei RHEA Group c/o European Space Agency (ESA) Frascati Italy Institute of Bio- and Geosciences IBG-2: Plant Sciences Forschungszentrum Jülich GmbH Julich Germany Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O 2 -A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O 2 -A band with DESIS and HyPlant.

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A scalable pipeline to create synthetic datasets from functional-structural plant models for deep learning (vol 6, diad022, 2024)

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IN SILICO PLANTS 2024年第1期6卷

作者： Baker, Dirk Norbert Bauer, Felix Maximilian Giraud, Mona Schnepf, Andrea Goebbert, Jens Henrik Scharr, Hanno Hvannberg, Ebba Thora Riedel, Morris School of Engineering and Natural Sciences Sæmundargata 2 102 Reykjavík Iceland Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany Institute of Bio- and Geosciences 3 (Agrosphere) Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany Institute for Advanced Simulation 8 (Data Analytics and Machine Learning) Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße 52428 Jülich Germany

In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducingSynavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.

关键词： Computer vision deep learning FSPM HPC synthetic data visualization

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