The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years

This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.

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Publication Details

Journal
GigaScience
Published
2025-01-01
DOI
https://doi.org/10.1093/gigascience/giaf051
Citations
6
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
10.30

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article

The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years

Helge Aasen, Andreas Hund, Jonas Anderegg, Flavian Tschurr et al.
6 citations
GigaScience
Remote Sensing in Agriculture
10.30
article

The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years

Helge Aasen, Andreas Hund, Jonas Anderegg, Flavian Tschurr, Kang Yu, Johannes Pfeifer, Hansueli Zellweger, Xiaoran Chen, Brenda Patricia Aguirre-Cuellar, Frank Liebisch, Norbert Kirchgeßner, Michele Volpi, Achim Walter, Joaquin Gajardo, Lukas Roth, Beat Keller, Lukas Kronenberg, Corina Oppliger, Paraskevi Nousi, Gregor Perich, Simon Treier, Olivia Zumsteg, Nicola Storni, Simon Corrado, Krzysztof Cybulski, Stefan Göbel Kortstee, Mike Boss, Price Pius Atuah Akiina
article en
6 citations

Abstract

This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.

GigaScienceVol. 14
ETH Zurich (CH), Instituto de Ciencias Agrarias (ES), Swiss Data Science Center (CH), École Polytechnique Fédérale de Lausanne (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Climate action
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
10.30
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