Skip Ribbon Commands
Skip to main content
Suche
Breadcrumb Navigation

Artificial Intelligence for Smart Agriculture

Hauptinhalt der Seite

Arrow left Back to the working groups of the research area

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

MyTitle: Classifying human influences on terrestrial ecosystems. Global Change Biology
MyLinkAnsehenUrl: https://doi.org/10.1111/gcb.15577, Resolve DOI name
MyLinkPDFUrl:
MyTitelMehrzeilig:
MyTextfeld:
MyAutoren: Rillig, M. C., Ryo, M., Lehmann, A. (2021)
MyMeldungsdatum:
MyLabels:
Struktureinheiten:Label: NewWindow:

Classifying human influences on terrestrial ecosystems. Global Change Biology

Rillig, M. C., Ryo, M., Lehmann, A. (2021)
MyTitle: Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models
MyLinkAnsehenUrl: https://doi.org/10.1111/ecog.05360, Resolve DOI name
MyLinkPDFUrl: https://publications.zalf.de/publications/f051dc70-ec87-4492-9fa2-3dd472c60904.pdf, PDF take a look / download
MyTitelMehrzeilig:
MyTextfeld:
MyAutoren: Ryo, M., Angelov, B., Mammola, S., Kass, J. M., Benito, B. M., Hartig, F. (2021)
MyMeldungsdatum:
MyLabels:
Struktureinheiten:Label: NewWindow:

Explainable artificial intelligence enhances the ecological interpretability of black-box species distribution models

Ryo, M., Angelov, B., Mammola, S., Kass, J. M., Benito, B. M., Hartig, F. (2021)
MyTitle: Rapid evolution of trait correlation networks during bacterial adaptation to the rhizosphere
MyLinkAnsehenUrl: https://doi.org/10.1111/evo.14202, Resolve DOI name
MyLinkPDFUrl:
MyTitelMehrzeilig:
MyTextfeld:
MyAutoren: Li, E., Ryo, M., Kowalchuk, G. A., Bakker, P. A. H. M., Jousset, A. (2021)
MyMeldungsdatum:
MyLabels:
Struktureinheiten:Label: NewWindow:

Rapid evolution of trait correlation networks during bacterial adaptation to the rhizosphere

Li, E., Ryo, M., Kowalchuk, G. A., Bakker, P. A. H. M., Jousset, A. (2021)

 

  

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

Fusszeile der Seite
Wordpress
YouTube
Twitter
Facebook
© Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e. V. Müncheberg