Hélène Soyeurt

Professor

University of Liege

Belgium

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Professor Hélène Soyeurt is a faculty member at the University of Liège in Belgium. Her research interests encompass dairy herd management, environmental sustainability in agriculture, and the application of advanced technologies such as machine learning and remote sensing in farming. Recent publications highlight her work on fatty acid profiles in dairy management, satellite-powered automation, and the prediction of dietary indicators in dairy cows. Professor Soyeurt’s contributions aim to enhance the efficiency and sustainability of agricultural practices.

Articles (13)

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2025

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2024

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2024

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2023

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2023

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2022

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2021

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2021

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2021

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2021

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2021

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BELGIUM

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Caroline De Clerck

Assistant Professor

University of Liege

BELGIUM

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Hélène Soyeurt

Research Interests

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Articles (13)

Detection of Dairy Herd Management Issues Using Fatty Acid Profiles Predicted by Mid-Infrared Spectrometry

Harvesting Insights from the Sky: Satellite-Powered Automation for Detecting Mowing Based on Predicted Compressed Sward Heights

Balancing Environmental Sustainability and Economic Viability in Luxembourgish Farms: An Agent-Based Model with Multi-Objective Optimization

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing

Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms

Volatile Organic Compounds Emitted by <i>Aspergillus flavus </i>Strains Producing or Not Aflatoxin B1

Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Appropriate Data Quality Checks Improve the Reliability of Values Predicted from Milk Mid-Infrared Spectra

Development of machine learning models to predict compressed sward height in walloon pastures based on sentinel-1, sentinel-2 and meteorological data using multiple data transformations

Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions

Mining data from milk mid-infrared spectroscopy and animal characteristics to improve the prediction of dairy cow's liveweight using feature selection algorithms based on partial least squares and Elastic Net regressions

Collaborators (6)

Pierre Manneback

Yves Brostaux

Jérôme Bindelle

Moad Hani

Nicolas Gengler

Caroline De Clerck

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