Roth Lukas, Boss Mike, Kirchgessner Norbert, Aasen Helge, Aguirre-Cuellar Brenda Patricia, Akiina Price Pius Atuah, Anderegg Jonas, Castillo Joaquin Gajardo, Chen Xiaoran, Corrado Simon, Cybulski Krzysztof, Keller Beat, Göbel Kortstee Stefan, Kronenberg Lukas, Liebisch Frank, Nousi Paraskevi, Oppliger Corina, Perich Gregor, Pfeifer Johannes, Yu Kang, Storni Nicola, Tschurr Flavian, Treier Simon, Volpi Michele, Zellweger Hansueli, Zumsteg Olivia, Hund Andreas, Walter Achim
ETH Zürich, Institute of Agricultural Sciences, 8092 Zürich, Switzerland.
ETH Zürich and EPFL, Swiss Data Science Center, 8092 Zürich and 1015 Lausanne, Switzerland.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf051.
Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
We provide time-series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across 6 years. Measurement data for 8 key wheat traits are included-namely, canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research.
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.
了解植物的基因型 - 环境相互作用对于作物改良至关重要，但受到高质量表型数据稀缺的限制。本数据说明展示了田间表型平台1.0数据集，这是一个用于冬小麦研究的综合资源，它结合了成像、性状、环境和遗传数据。
我们提供了4000多个小麦地块的时间序列数据，包括6年期间总计超过153,000张对齐的高分辨率图像序列。包含了8个关键小麦性状的测量数据，即冠层覆盖值、株高、麦穗数、衰老等级、抽穗期、最终株高、籽粒产量和蛋白质含量。遗传标记信息和环境数据补充了时间序列。通过遗传力分析和基因组预测模型证明了数据质量，所达到的准确性与先前的研究一致。
这个广泛的数据集为推进作物建模和表型分析技术提供了机会，使研究人员能够开发新方法来理解基因型 - 环境相互作用、分析生长动态并预测作物表现。通过公开提供此资源，我们旨在加速气候适应性农业的研究，并促进植物科学和机器学习社区之间的合作。
