Florian Haselbeck
Florian Haselbeck
Professor for Smart Farming at University of Applied Sciences Weihenstephan-Triesdorf
Verified email at - Homepage
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Machine learning outperforms classical forecasting on horticultural sales predictions
F Haselbeck, J Killinger, K Menrad, T Hannus, DG Grimm
Machine Learning with Applications 7, 100239, 2022
Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields
N Genze, R Ajekwe, Z Güreli, F Haselbeck, M Grieb, DG Grimm
Computers and Electronics in Agriculture 202, 107388, 2022
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species
M John, F Haselbeck, R Dass, C Malisi, P Ricca, C Dreischer, ...
Frontiers in Plant Science 13, 932512, 2022
EVARS-GPR: EVent-triggered augmented refitting of gaussian process regression for seasonal data
F Haselbeck, DG Grimm
KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI …, 2021
Dynamically self-adjusting Gaussian processes for data stream modelling
JD Hüwel, F Haselbeck, DG Grimm, C Beecks
German Conference on Artificial Intelligence (Künstliche Intelligenz), 96-114, 2022
Inter-individual variability of eeg features during microsleep events
M Golz, A Schenka, F Haselbeck, MP Pauli
Current Directions in Biomedical Engineering 5 (1), 13-16, 2019
Superior protein thermophilicity prediction with protein language model embeddings
F Haselbeck, M John, Y Zhang, J Pirnay, JP Fuenzalida-Werner, ...
NAR Genomics and Bioinformatics 5 (4), lqad087, 2023
ForeTiS: A comprehensive time series forecasting framework in Python
J Eiglsperger, F Haselbeck, DG Grimm
Machine Learning with Applications 12, 100467, 2023
Forecasting seasonally fluctuating sales of perishable products in the horticultural industry
J Eiglsperger, F Haselbeck, V Stiele, CG Serrano, K Lim-Trinh, K Menrad, ...
Expert Systems with Applications, 123438, 2024
Time Series Forecasting with Self-Adaptive Gaussian Process Regression
F Haselbeck
Technische Universität München, 2023
easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization
F Haselbeck, M John, DG Grimm
Bioinformatics Advances 3 (1), vbad035, 2023
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