Dataset Card for GrowliFlower

This is a FiftyOne dataset with 24,866 samples (across 23,875 groups, group slices pre and post).

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fo.load_from_hub("Voxel51/GrowliFlower")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

GrowliFlower is a georeferenced, image-based UAV time-series dataset of two monitored cauliflower fields near Cologne, Germany (Field 1, 2020, 0.39 ha; Field 2, 2021, 0.60 ha), captured with a 47.4 MP Sony A7rIII RGB camera and a MicaSense 5-channel multispectral camera. This FiftyOne dataset consolidates three of the source release's task-specific subsets into one grouped dataset: pixel-accurate plant/leaf/stem instance segmentations on independent image patches, phenotyped-plant growth time series with real measured traits (height, diameter, BBCH growth stage, harvest status), and defoliation plant time series with a paired pre/post leaf-removal capture that exposes the cauliflower head.

• Curated by: Jana Kierdorf, Laura Verena Junker-Frohn, Mike Delaney, Mariele Donoso Olave, Andreas Burkart, Hannah Jaenicke, Onno Muller, Uwe Rascher, and Ribana Roscher (University of Bonn, Forschungszentrum Jülich, JB Hyperspectral Devices GmbH)
• Funded by: European Agriculture Fund for Rural Development, with contribution from North-Rhine Westphalia (17-02.12.01 - 10/16 – EP-0004617925-19-001); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2070 – 390732324
• Shared by: This FiftyOne-formatted version was curated and published by Harpreet Sahota
• Language(s): en
• License: [More Information Needed] — no license is stated in the PhenoRoam catalog record, the shipped README, or the source paper. Confirm terms with the corresponding author (Jana Kierdorf, jkierdorf@uni-bonn.de) before redistribution.

Dataset Sources

• Repository: PhenoRoam catalog record
• Paper: GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER (arXiv:2204.00294)
• Demo: [More Information Needed]

Uses

Direct Use

• Training and evaluating instance segmentation, object detection, and stem-keypoint detection models on cauliflower plant/leaf imagery (segmentation task samples)
• Growth-curve and phenotype-trait regression: predicting height, diameter, developmental stage, or head diameter from imagery, using the reference task samples and their joined in_situ_* fields
• Studying the correlation between external plant appearance (leaves/canopy) and the internal cauliflower head, using the defoliation task's paired pre/post group slices
• Tracking a single plant's growth trajectory over time via its plant_id field, for both the reference and defoliation tasks

Out-of-Scope Use

• This FiftyOne dataset does not include GrowliFlowerT (the source release's third time-series subset — 158,412 unlabeled random-plant crops with no in-situ measurements), nor the raw RGB/multispectral orthomosaics (GrowliFlowerO/GrowliFlowerM) themselves — those remain multi-gigabyte GeoTIFFs unsuited to direct per-sample FiftyOne import. Whole-field mapping or orthomosaic-level analysis is out of scope for this version.
• Given the unresolved license status, this dataset should not be used for commercial redistribution without confirming terms with the original authors first.

Dataset Structure

This is a grouped FiftyOne dataset (media_type="group"): 24,866 samples across 23,875 groups, with group slices pre (23,889 samples, default) and post (977 samples). Every group represents one plant at one acquisition date; a group has only a pre slice on ordinary dates, and gains a second post slice only on a defoliation plant's actual defoliation date (leaves removed to expose the cauliflower head). Segmentation patches have no natural pairing with anything else, so each forms its own singleton pre-only group.

The dataset combines three tasks from the original release, distinguished by the task field:

task value	Samples	Source subset	Description
segmentation	2,198	GrowliFlowerL	Independent 448x368 sliding-window patches (Field 1, 2020 only) with pixel-accurate instance masks
reference	9,377	GrowliFlowerR	Crops of 740 phenotyped plants across up to 15 acquisition dates each, with real measured traits
defoliation	13,291	GrowliFlowerD	Crops of defoliated plants: a pre-defoliation time series per plant, plus one post-defoliation capture

Splits are stored as sample tags (train: 15,142, val: 5,052, test: 4,672), following each source subset's own proposed, spatially-disjoint split assignment.

Four saved views are included for quick access to each task:

View	Samples	Description
segmentation	2,198	All segmentation task samples
reference_growth	9,377	All reference task samples
defoliation_pairs	13,291	All defoliation task samples, across both pre and post slices
defoliation_post_only	977	Just the post group slice, for pairing against each group's pre sample

Fields

Field	FiftyOne type	Description
filepath	StringField	Absolute path to the cropped JPEG image on disk for this sample
tags	ListField(StringField)	Split assignment (train/val/test) from the source subset's proposed spatially-disjoint split
metadata	EmbeddedDocumentField(ImageMetadata)	Standard FiftyOne image metadata (width, height, size_bytes, mime_type, num_channels)
group	EmbeddedDocumentField(Group)	Groups one plant's captures at one acquisition date together; see Dataset Structure above
task	StringField	Which task this sample supports: segmentation, reference, or defoliation
field	StringField	Which monitored field: Field1 (2020) or Field2 (2021)
acquisition_date	StringField	Calendar date (YYYY-MM-DD) the source orthomosaic was captured, or the segmentation patch was extracted
patch_id	StringField	Sliding-window tile index (segmentation only) — arbitrary, not linked to any tracked plant
has_plants	BooleanField	Whether a segmentation patch has any annotated plant instance (~10% deliberately show bare soil)
plant_instances	Detections	Per-plant instance masks (segmentation only), decoded from the source maskPlants PNGs; each detection carries an is_void attribute for plants cut off at the patch border
leaf_instances	Detections	Per-leaf instance masks (segmentation only), decoded from maskLeaves; void plants are fully excluded from this mask by the original annotators
stem_keypoints	Keypoints	One point per non-void plant's stem position (segmentation only), decoded from maskStems
void_mask	Segmentation	Binary mask of plants cut off at the patch border (segmentation only), decoded from maskVoid
plant_id	StringField	Stable identifier for one tracked plant (reference/defoliation only), constant across every date it was photographed
phase	StringField	pre (default) or post (defoliation-day, after leaf removal)
day_after_planting	IntField	Days between this plant's planting date and this sample's acquisition date
planting_date	StringField	Calendar date (YYYY-MM-DD) this plant was planted
coverage_frac	FloatField	Fraction of non-zero (valid) pixels in the crop; low values flag a documented orthomosaic data gap for that date
plot	IntField	Reference plot number (reference only): 1-4 for Field 1, 1-5 for Field 2
harvest_timepoint	StringField	Harvest-window index for this reference plant (reference only), from the source coordinate file
in_situ_height	StringField	Manually measured plant height (reference only)
in_situ_diameter	StringField	Manually measured plant diameter (reference only)
in_situ_bbch_stage	StringField	Phenological development stage, BBCH/Feller 1995 scale (reference only)
in_situ_head_diameter	StringField	Manually measured cauliflower head/curd diameter (reference only)
in_situ_harvested	StringField	Harvest marker once the plant was harvested (reference only); null before harvest
in_situ_comment	StringField	Free-text field observation (reference only), using abbreviations documented in the source abbreviation sheet
day_group	IntField	Defoliation-day group index (defoliation only): day1-day2 for Field 1, day1-day6 for Field 2
defoliation_date	StringField	Calendar date (YYYY-MM-DD) this plant was defoliated (defoliation only)

All six in_situ_* fields are stored as strings rather than numbers: the source spreadsheet mixes clean numeric values with free-text/range entries in the same column (e.g. a diameter recorded as "10 (10)"), and casting to a numeric type would silently corrupt or drop those entries.

dataset.info["source"] records the provenance string: "Consolidated from GrowliFlowerL, GrowliFlowerR, and GrowliFlowerD subsets of GrowliFlower (PhenoRoam catalog record, arXiv:2204.00294)."

Parsing decisions

• Segmentation labels (plant_instances/leaf_instances/stem_keypoints/void_mask) were decoded from indexed-color PNG instance masks by finding each unique non-zero index, taking its bounding box and boolean sub-mask (for Detections), or its pixel centroid (for the single-point Keypoints on stems).
• Crop windows for reference/defoliation samples are always derived from each plant's global UTM coordinate via the orthomosaic's own affine transform (rasterio.transform.rowcol), never from the source release's local per-date pixel-coordinate files — one of those files (GrowliFlowerD/Field 1) contains corrupted pixel values (up to ~9.7M px on a ~46K-px-wide image), so a single uniform, UTM-based method was used everywhere instead of special-casing that file.
• Some source orthomosaics are uint16, not uint8, with real values spanning well past a 12-bit range — a plain downcast produces wraparound noise. A per-orthomosaic (not per-patch) 1st/99th-percentile contrast stretch is applied before cropping, so contrast stays consistent across every patch cut from the same source image.
• In-situ phenotype measurements are joined onto reference samples by (plot, row, position, acquisition date), parsed from the source Excel workbooks by header name rather than column position, since Field 1 and Field 2 use different column orders and even different column names for the same trait (e.g. Ø Head vs Ø Flower).
• defoliation samples are not a simple 2-shot pre/post pair: each defoliated plant actually has a full pre-defoliation time series (many acquisition dates) plus one additional post-defoliation capture. This is why grouping is keyed by (plant_id, acquisition_date) rather than by plant_id alone — every date gets its own group, and only the true defoliation date's group has both a pre and a post slice.
• Crops with coverage_frac well below 1.0 (4.1% of reference/defoliation samples) were kept, not dropped, since they correspond to a real, paper-documented data gap in specific Field 1 orthomosaics rather than a processing error.

Dataset Creation

Curation Rationale

The source dataset was created to foster machine learning approaches for analyzing crop growth and development and deriving phenotypic traits relevant to agricultural automation — cauliflower in particular, because its harvestable product (the head) is fully obscured by leaves until harvest, making yield and quality estimation from external imagery a genuinely hard, unsolved problem. This FiftyOne-formatted version further consolidates the source release's segmentation and time-series subsets into one dataset so both can be explored and cross-referenced (e.g. training a segmentation model on one task and evaluating growth-trait extraction on another) from a single FiftyOne session.

Source Data

Data Collection and Processing

RGB imagery was captured with a Sony A7rIII (47.4 MP, Zeiss/Batis 25mm lens) and multispectral imagery with a MicaSense 5-channel camera (475/560/668/717/840 nm bands), flown by UAV over both fields roughly weekly across the full growing season, from planting to harvest. Georeferenced ground control points (21 in Field 1, 44 in Field 2) were used to align and export the images as RGB and multispectral orthomosaics via Agisoft Metashape Professional, in WGS84 / UTM 32N. Individual plant coordinates were derived from the orthomosaics and used, together with manually recorded in-situ measurements, to build the source release's task-specific subsets. For this FiftyOne version, patches were cropped from the RGB orthomosaics at each plant's UTM coordinate (490x490 px for Field 1, 256x256 px for Field 2), and the source subset's own instance-segmentation labels and in-situ measurement spreadsheets were parsed and attached as described above.

Who are the source data producers?

UAV image acquisition and in-situ measurements were carried out by the paper's authors (University of Bonn, Forschungszentrum Jülich, JB Hyperspectral Devices GmbH) on a commercial cauliflower field near Cologne, Germany, in cooperation with the farmer Markus Schwarz.

Annotations

Annotation process

Pixel-wise plant, leaf, void, and stem instance annotations (segmentation task) were manually labeled by the dataset authors. In-situ phenotypic measurements (reference task's in_situ_* fields) — plant height, diameter, BBCH developmental stage, head diameter, and harvest status — were recorded manually in the field at each measured acquisition date.

Who are the annotators?

The paper's authors and their field team at the University of Bonn / Forschungszentrum Jülich.

Personal and Sensitive Information

None. The dataset consists exclusively of aerial agricultural imagery and plant phenotype measurements; it contains no personal or sensitive information.

Citation

BibTeX:

@article{kierdorf2022growliflower,
  title={GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER},
  author={Kierdorf, Jana and Junker-Frohn, Laura Verena and Delaney, Mike and Olave, Mariele Donoso and Burkart, Andreas and Jaenicke, Hannah and Muller, Onno and Rascher, Uwe and Roscher, Ribana},
  journal={arXiv preprint arXiv:2204.00294},
  year={2022}
}

APA:

Kierdorf, J., Junker-Frohn, L. V., Delaney, M., Olave, M. D., Burkart, A., Jaenicke, H., Muller, O., Rascher, U., & Roscher, R. (2022). GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER. arXiv preprint arXiv:2204.00294.

More Information

This FiftyOne dataset consolidates 3 of the source release's 4 machine-learning-oriented subsets (GrowliFlowerL, GrowliFlowerR, GrowliFlowerD); GrowliFlowerT was not included. The raw RGB and multispectral orthomosaics (GrowliFlowerO, GrowliFlowerM) are not included as samples either, since they are multi-gigabyte whole-field GeoTIFFs used only as the crop source. A full reconnaissance report — including the corrupted-coordinate-file, inconsistent-orthomosaic-dtype, and in-situ-spreadsheet-layout findings summarized above — is available in DATASET_INSPECTION.md alongside the source download.

Dataset Card Authors

Harpreet Sahota