Key Note: Dr. Martin Junghans, CTO Innovation Studios, IBM Deutschland GmbH
|Nora Gourmelon, FAU Erlangen-Nürnberg |
AI-based Remote Sensing Applications
What do bird populations, glaciers and water consumption have in common? We can monitor them using remote sensing. To reduce manual effort, AI-based methods are being developed to take over this task
|Siming Bayer, FAU Erlangen-Nürnberg |
|Emanuel Jakobowsky, Technical University of Applied Sciences Ingolstadt |
Detecting and counting wheat spike heads from UAV-based images using deep convolutional neural networks
Manual detection and counting of wheat head spikes is essential for maximizing wheat yields but this task is also time-consuming and subject to human error. This talk presents a plot-level analysis using a Mask R-CNN for automatic detection and counting of wheat spike heads on UAV-based images.
- Dimo Dimov, University of Applied Sciences Karlsruhe
Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation
In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation.
|Raphael Oefelein, HSWT Standort Triesdorf |
3D Crop Modeling with Neural Radiance Fields and Hyperspectral Imaging for Vegetation Index Computation
In today’s agricultural landscape, numerous complex challenges such as sustainable resource utilization,
environmental impact mitigation, and the need for more efficient production methodologies
have emerged. In this context, the creation of digital twins for crop plants presents new opportunities
for agricultural digitization. Realistic simulations aim to assist farmers in basing their
management practices on data-driven insights, optimizing resource utilization, while simultaneously
minimizing environmental impacts1. The development of a lifelike 3D object stands as a
pivotal step in the evolution of a comprehensive digital twin.
|Andreas Gilson, Fraunhofer IIS |
Digitizing Reality: AI as a Key Ingredient in the Creation of a complex Digital Twin
Digital Twins of real-world objects and processes continue to gain importance in various fields of application. A digital representation of a plant can be an invaluable asset for e.g. researchers and breeders and in the future also for producers. The ability to precisely capture the state of a plant at any time and analyze, assess and predict the phenotypic development - from architecture to fruit size – promises new insights. We describe the role of AI in the development of a digital twin of a cherry tree.
|Dr. Sebastian Fritsch, HSWT Standort Triesdorf |
More than ChatGPT: The benefits of Large Language Models for agriculture
|Prof. Dr. Dominik Grimm, HSWT Standort Straubing |
Advancing Protein Engineering with Large Language Models
Accurate prediction of protein properties is an essential task in many areas of biotechnology, including enzyme engineering and protein-hybrid optoelectronics. In this talk, we will show the benefits of large language models for predicting protein thermophilicity and thermostability, and give an outlook on how these models will revolutionize the design of synthetic proteins.