Update README.md

README.md

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 - pheno
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 - 1K<n<10K
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+---
+# The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years
+
+## Dataset Details
+
+### Dataset Description
+
+We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 151,000 aligned images across six years.
+Measurement data for eight key wheat traits is 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.
+
+
+- **Curated by:** Mike Boss, Lukas Roth, Norbert Kirchgessner
+- **License:** CC-BY
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Data:** [doi.org/20.500.11850/697773](doi.org/20.500.11850/697773)
+- **Paper:** Coming soon!
+
+## Uses
+
+We see the largest re-use potential of the presented data set for the development and evaluation of new modelling and prediction approaches in crop genomics and phenomics.
+The multi-faceted data set allows modelling approaches on various levels:
+- Genomic prediction approaches that include genotype-environment interactions: The presented data enhance the data by Gogna et al. by 6 environments, totalling to 14 environments that are characterized by environmental covariates. The presented benchmark of a genomic prediction with random regressions to environmental covariates  provides a baseline that novel approaches can challenge.
+- Modelling plant growth and development with longitudinal modelling approaches: The four low-level traits canopy cover, plant height, wheat head count and senescence cover the full growing season of winter wheat in 6 environments that are characterized by environmental covariates. Baseline approaches for plant height growth modelling, canopy cover growth modelling and senescence dynamics modelling for subsets of the presented data exist.
+- Image-based phenomic predictions and combined phenomic and genomic prediction approaches: The dense time series of images allow training and analysing end-to-end modelling approaches (e.g., deep learning based) that predict target traits such as yield based on images.
+
+## Dataset Structure
+
+The data is structured such that each row contains all data for a plot in a given year.
+The plot can be identified by it's *plot_uid* which is also available in parts as *yearsite_uid*, *crop_type*, etc.
+If data does not exist for a certain plot it is *None*, while if time series data does not exist for a certain date it is simply not present.
+
+The image data is in the *images* and *inner_plot_images* colums.
+*images* are the original images while *inner_plot_images* are aligned cutouts of the inner wheat plot.
+Trait data is included as the values in *trait_value(s)* and the dates *trait_date(s)*.
+Marker data includes an anyomized *genotype_id* string, biallelic codes and corresponding metadata strings.
+Enviroment variables are also included as *_value(s)*, *_date(s)* and in addition *_time(s)*.
+
+The sowing date, harvest date and harvest_year are included.
+The data set includes additional data used for the creation of the data set itself such as the alignments.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Winter wheat provides a crucial share of calories for human nutrition, with global demand steadily increasing. 
+However, crop production faces challenges due to limited resources like water, agrochemicals, and land. 
+Climate change further threatens crop yields, necessitating responsible and efficient resource use.
+
+Crop yields are substantially driven by complex interactions between plant genetics and environmental factors.
+For instance, genes involved in fruit formation interact with temperatures at flowering, influencing growth and yield potential. 
+Limited phenotyping data is seen as the major reason for the incomplete understanding of such genotype-environment interactions.
+
+HTFP was developed to address this data gap.
+Imaging HTFP platforms allow researchers to monitor crop canopy development over time, generating dense time series data of plant growth. 
+There are many approaches to process such data ranging from extracting traits at critical time points to modeling growth dynamics and finally using end-to-end methods that directly analyze image time series.
+
+This data set aims to provide a comprehensive foundation for these diverse approaches. 
+Our goal is to foster collaboration between plant physiology, biometrics, and computer vision research, ultimately improving the ability to predict genotype-environment interactions for current and future climates.
+
+### Source Data
+
+The FIP at ETH was established in 2015 to collect image time series of crops growing under realistic field conditions.
+The FIP's cable carrying system is capable of carrying a 90 kg sensor head.
+The original sensor head, hereafter referred to as the FIP 1.0 head, was equipped with a red, green, and blue (RGB) camera and a TLS, among other sensors.
+Wheat field experiments were observed using FIP 1.0 over an eight-year period from 2015 to 2022, yielding six years of data collection, with 2015 and 2020 excluded due to incomplete measuring seasons .
+Up to three times a week, RGB images of all experimental units (so-called `plots') were collected, and plant heights were measured simultaneously using either the TLS (2016, 2017) or drones (2018--2022) .
+In 2023, the FIP 1.0 sensor head was replaced with a new, multi-view RGB sensor head.
+The described data set includes all RGB and height data collected in winter wheat experiments up to this replacement.
+
+The area of approximately one ha that the FIP can monitor is divided into six smaller parts (so-called `lots') that are integrated into a crop rotation. 
+The two FIP lots dedicated to winter wheat provide space for ~350 genotypes, replicated once per lot.
+For the first three years (2016--2018), the GABI-WHEAT panel was grown as the genotype set.
+From 2019--2022, a subset of the GABI-WHEAT panel was grown in addition to other genotypes .
+The GABI-WHEAT panel consists of registered genotypes from different climatic regions of Europe .
+Genetic marker data and MET data from eight year-locations for GABI-WHEAT are publicly available.
+
+The GABI-WHEAT panel was largely superseded by the Swiss breeding set in 2021 . 
+This new set primarily consists of eighth-generation (F8) breeding genotypes.
+For the Swiss breeding set, genetic marker data exists but remains confidential.
+The remaining genotypes, linked to specific projects such as INVITE, were present throughout all years but were generally only grown in a single year each . 
+These genotypes currently lack available marker data.
+
+Regular measurements with the FIP 1.0 head were accompanied by reference measurement campaigns as part of several projects.
+The heading date and senescence ratings were performed to investigate the relationships of senescence dynamics and diseases .
+Yield measurements taken on the FIP field were combined with data from other locations to train phenomic prediction models .
+The plant height measurements served as a basis to quantify the temperature response of wheat genotypes in the stem elongation phase .
+The extracted plant height values demonstrated their usefulness in improving trait extraction methods from longitudinal data .
+
+The images collected were used to quantify canopy covers and examine their relationship to frost damage events using CNNs.
+Using a combination of drone data and the high-resolution images the rows in the individual plots were identified .
+In a small subset (375 images), the wheat heads were annotated and the data was integrated into the public global wheat head detection data set .
+The image-based canopy cover values served as a test data set to evaluate the cultivar-specific extensions of the thermal time concept .
+
+
+<!--
+### Annotations [optional]
+
+
+#### Annotation process
+
+
+[More Information Needed]
+
+
+
+[More Information Needed]
+
+
+## Citation [optional]
+
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+[More Information Needed]
+-->
+
+
+## Dataset Card Authors
+
+Mike Boss, Lukas Roth, Norbert Kirchgessner
+
+## Dataset Card Contact
+
+Mike Boss ([email protected])