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Artificial Intelligence for Smart Agriculture

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A sustainable development of agriculture has great potential to alleviate various global challenges, including biodiversity loss, food and water security, as well as climate change. We believe that data science can facilitate this process. The working group is committed to developing and applying cutting-edge data-driven techniques such as interpretable machine learning and deep learning for a broad range of agriculture-related problems. We are interested in exploring the potential of artificial intelligence for improving agricultural management, testing novel hypotheses that cannot be tested with conventional statistical methods, and discovering unexpected patterns from uniquely combined datasets across scales and sectors. We wish to offer AI-powered, Nature-based Solutions for global sustainability challenges.

 

 

 Publications WG

 

 Projects WG

MyTitle: KIKompAG - Multi-modal data integration, domain-specific methods and AI to strengthen data literacy in agricultural research
MyLinkAnsehenUrl: https://www.zalf.de/en/forschung_lehre/projekte/Pages/details.aspx, Take a look
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MyTextfeld: Start: 01.10.2022
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KIKompAG - Multi-modal data integration, domain-specific methods and AI to strengthen data literacy in agricultural research

Start: 01.10.2022

 

  

Contact

 

Head of working group

Prof. Dr. Masahiro Ryo
T +49 (0)33432 82-206

 

Address

Leibniz-Zentrum für
Agrarlandschaftsforschung (ZALF) e. V.

Eberswalder Straße 84
15374 Müncheberg

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