ason, therefore, from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the field. The factor to convert radiation in MJ m− 2 to PPFR was 2.04 according to [26]. 4.1 Data Files and Structure The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable and widely-used ‘.csv‘ and ‘.png‘ format. • data/Design 2015 2022 Eschikon.csv: Experimental design file, including p
Open resource ↗ETH Research Collection · 10.3929/ethz-b-000742401 · pdf-raw-page:6 lines:1-47Unverified paper record
FIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Research Square · 1 Aug 2025 · 10.21203/rs.3.rs-7263188/v1
Abstract
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. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over 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 enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Plant phenotyping relevance
8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
abstractHere, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland.
abstractTop 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.
Code and data availability
The paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, and
Collection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing interoperability and standardization with other datasets. 9 Code availability Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover 10 Author contributions BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK, CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design: BK, LK, LR, AH; all authors improved and approved the manuscript 5
Open resource ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycover · pdf-raw-page:7 lines:1-46This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.