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}
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.
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.