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. ...
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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 t...
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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 c...
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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 liv...
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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 ...
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.
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, sin...
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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.
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 r...
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 machinelearning (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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