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.
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Code availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.
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Acknowledgements
We thank Agroscope Soybean Breeding and Delley seeds and plants Ltd. (DSP) for providing the seeds. We would also like to thank Brigitta Herzog for seed preparation and inoculation. Special thanks go to Mike Boss for making the data set available on hugging face. We would like to thank the entire Crop Science Group at ETH Zürich for their support in and out of the field and for their engagement in discussions about the manuscript. Large language model (ChatGPT/Claude) were used in part to assist with coding and phrasing. All outputs were checked, verified and finalized by the authors. This work was supported by the Swiss National Science Foundation (Grant Nos. 200756 and 169542).
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B.K.: Developed algorithm, analyzed data and drafted manuscript; N.K., L.R., A.H., A.M.: FIP development, B.K., N.K., C.O., L.K., L.R., O.Z., S.C., F.L., H.A., N.S., F.T., H.Z., C.A.B., C.B., A.H.: Collected and prepared data; Experimental design: B.K., L.K., L.R., A.H.; all authors improved and approved the manuscript
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Keller, B., Kirchgessner, N., Oppliger, C. et al. FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping. Sci Data (2026). https://doi.org/10.1038/s41597-026-06663-z
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DOI: https://doi.org/10.1038/s41597-026-06663-z
