Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
8 Ergebnisse
Dr. Fabian Weckesser,
Marcus Albrecht,
Prof. Dr. Kurt-Jürgen Hülsbergen,
Prof. Dr. Frank Leßke
Nährstoffeffiziente Fruchtfolgen durch erfolgreiches Leguminosen-Gras Management (2024) FIBL Deutschland e.V. (Hg.): Proceedings der 17. Wissenschaftstagung Ökologischer Landbau. 5.-8. März 2024. Gießen .
Carbon sequestration in soils under agricultural use can contribute to climate change mitigation.Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition.This study aims to evaluate the potential of spectral on-the-go proximal measurements toserve these needs. The study was conducted as a long-term field experiment. SOC values rangedbetween 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regressionmodels were built based on the spectral laboratory and field data collected with two spectrometers(site-specific and on-the-go). Correction of the field data based on the laboratory data was done bytesting linear transformation, piecewise direct standardization, and external parameter orthogonalization(EPO). Different preprocessing methods were applied to extract the best possible informationcontent from the sensor signal. The models were then thoroughly interpreted concerning spectralwavelength importance using regression coefficients and variable importance in projection scores.The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy forSOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts inwavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporalSOC monitoring are promising.
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Anika Gebauer,
Prof. Dr. Mareike Ließ,
Matteo Poggio,
Axel Don,
Ali Sakhaee
Berechtigungen: Open Access
Spatial topsoil texture predictions: Topsoil texture regionalization for agricultural soils in Germany – an iterative approach to advance model interpretation (2024) Open Science Framework .
DOI: 10.17605/OSF.IO/9DGB6
Nikita Genze,
Wouter K Vahl,
Jennifer Groth,
Maximilian Wirth,
Michael Grieb,
Prof. Dr. Dominik Grimm
Sustainable weed management strategies are critical to feeding the world’s population while preserving ecosystems and biodiversity. Therefore, site-specific weed control strategies based on automation are needed to reduce the additional time and effort required for weeding. Machine vision-based methods appear to be a promising approach for weed detection, but require high quality data on the species in a specific agricultural area. Here we present a dataset, the Moving Fields Weed Dataset (MFWD), which captures the growth of 28 weed species commonly found in sorghum and maize fields in Germany. A total of 94,321 images were acquired in a fully automated, high-throughput phenotyping facility to track over 5,000 individual plants at high spatial and temporal resolution. A rich set of manually curated ground truth information is also provided, which can be used not only for plant species classification, object detection and instance segmentation tasks, but also for multiple object tracking.
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Azamat Azarov,
Roy C. Sidle,
Prof. Dr. Dietrich Darr,
Vladimir Verner,
Zbynek Polesny
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