FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping

Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-02-18
DOI
https://doi.org/10.1038/s41597-026-06663-z
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping

Helge Aasen, Flavian Tschurr, Hansueli Zellweger, Frank Liebisch et al.
Scientific Data
Soybean genetics and cultivation
article

FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping

Helge Aasen, Flavian Tschurr, Hansueli Zellweger, Frank Liebisch, Christoph Barendregt, Lukas Roth, B Keller, Corina Oppliger, Achim Walter, Olivia Zumsteg, Nicola Storni, Simon Corrado, Andreas Hund, Claude-Alain Bétrix, Norbert Kirchgessner, Lukas Kronenberg
article en

Abstract

Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.

Scientific DataVol. 13(1)
ETH Zurich (CH), Agroscope (CH), Institute of Crop Science (JP), Delley Seeds and Plants (Switzerland) (CH)
Climate action
Openalex Percentile: Top 51%
Soybean genetics and cultivation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.