FIP 1.0数据集:高分辨率注释图像时间序列,4000块小麦地块在6年内生长。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Lukas Roth, Mike Boss, Norbert Kirchgessner, Helge Aasen, Brenda Patricia Aguirre-Cuellar, Price Pius Atuah Akiina, Jonas Anderegg, Joaquin Gajardo Castillo, Xiaoran Chen, Simon Corrado, Krzysztof Cybulski, Beat Keller, Stefan Göbel Kortstee, Lukas Kronenberg, Frank Liebisch, Paraskevi Nousi, Corina Oppliger, Gregor Perich, Johannes Pfeifer, Kang Yu, Nicola Storni, Flavian Tschurr, Simon Treier, Michele Volpi, Hansueli Zellweger, Olivia Zumsteg, Andreas Hund, Achim Walter
{"title":"FIP 1.0数据集:高分辨率注释图像时间序列,4000块小麦地块在6年内生长。","authors":"Lukas Roth, Mike Boss, Norbert Kirchgessner, Helge Aasen, Brenda Patricia Aguirre-Cuellar, Price Pius Atuah Akiina, Jonas Anderegg, Joaquin Gajardo Castillo, Xiaoran Chen, Simon Corrado, Krzysztof Cybulski, Beat Keller, Stefan Göbel Kortstee, Lukas Kronenberg, Frank Liebisch, Paraskevi Nousi, Corina Oppliger, Gregor Perich, Johannes Pfeifer, Kang Yu, Nicola Storni, Flavian Tschurr, Simon Treier, Michele Volpi, Hansueli Zellweger, Olivia Zumsteg, Andreas Hund, Achim Walter","doi":"10.1093/gigascience/giaf051","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12153353/pdf/","citationCount":"0","resultStr":"{\"title\":\"The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.\",\"authors\":\"Lukas Roth, Mike Boss, Norbert Kirchgessner, Helge Aasen, Brenda Patricia Aguirre-Cuellar, Price Pius Atuah Akiina, Jonas Anderegg, Joaquin Gajardo Castillo, Xiaoran Chen, Simon Corrado, Krzysztof Cybulski, Beat Keller, Stefan Göbel Kortstee, Lukas Kronenberg, Frank Liebisch, Paraskevi Nousi, Corina Oppliger, Gregor Perich, Johannes Pfeifer, Kang Yu, Nicola Storni, Flavian Tschurr, Simon Treier, Michele Volpi, Hansueli Zellweger, Olivia Zumsteg, Andreas Hund, Achim Walter\",\"doi\":\"10.1093/gigascience/giaf051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":12581,\"journal\":{\"name\":\"GigaScience\",\"volume\":\"14 \",\"pages\":\"\"},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2025-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12153353/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GigaScience\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/gigascience/giaf051\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giaf051","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

背景:了解植物基因型与环境的相互作用对作物改良至关重要,但由于缺乏高质量的表型数据而受到限制。本数据说明介绍了田间表型平台1.0数据集,这是冬小麦研究的综合资源,结合了成像,性状,环境和遗传数据。研究结果:我们提供了超过4000块小麦地块的时间序列数据,包括6年间超过15.3万张的高分辨率图像序列。包括8个关键小麦性状的测量数据,即冠层盖度、株高、穗数、衰老等级、抽穗日期、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,实现了与先前研究一致的准确性。结论:这一广泛的数据集为推进作物建模和表型技术提供了机会,使研究人员能够开发新的方法来理解基因型-环境相互作用,分析生长动态,预测作物性能。通过公开这些资源,我们的目标是加速气候适应性农业的研究,促进植物科学和机器学习社区之间的合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.

Background: 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.

Findings: 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.

Conclusions: 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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信