Agriculture can make a significant contribution to biodiversity management through digitalization approaches such as precision and smart farming with the data it collects and collects, its areas and an evaluation supported by machine learning (ML). The main objective of this project is to make use of this contribution. So far, the potentials of supporting biodiversity management through information from the agricultural sector have not been sufficiently analysed and exploited.
The project aims to address this deficit with a combination of innovative and established approaches both on the data provision side and on the data analysis and forecasting model side. A particular challenge arises in biodiversity issues due to the comparatively small number of data sets. The generation of the highest possible number of data sets and the application of ML algorithms, which allow the integration of expert knowledge in order to generate meaningful models and forecasts despite a small data volume, are therefore also central questions of the project.
Projectduration: 01.01.2025 – 31.12.2027
Projektpartner
Technische Hochschule Ostwestfalen-Lippe
Helmut Schmidt Universität / Universität der Bundeswehr Hamburg
Institut für Agrarökologie und Biodiversität (IFAB)
Letzte Änderung: 27. July 2025