Dataset Card for AppleGrowthVision

This is a FiftyOne dataset with 11,397 groups (21,407 samples) of apple orchard imagery.

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 = load_from_hub("harpreetsahota/AppleGrowthVision")

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

Dataset Details

Dataset Description

AppleGrowthVision is a large-scale, longitudinal dataset of apple orchard imagery collected at two German field sites across one or more full growth cycles. It combines two capture modalities: a fixed, fully-calibrated stereo rig (Brandenburg / "BB Obst" farm, Wesendahl) and handheld smartphone imagery (Saxony / Pillnitz orchard, near Dresden). Unlike prior apple-detection datasets, every image is tied to a specific point on the extended BBCH scale for pome fruit (bud development through senescence), and a subset of images is densely annotated with apple bounding boxes. Ten Pillnitz trees also carry a manually verified (non-destructive) reference apple count, giving a small yield-estimation ground truth in addition to the detection labels.

• Curated by: Laura-Sophia von Hirschhausen, Jannes S. Magnusson, Mykyta Kovalenko, Fredrik Boye, Tanay Rawat, Peter Eisert, Anna Hilsmann, Sebastian Pretzsch, Sebastian Bosse (Fraunhofer HHI and Fraunhofer IVI)
• Funded by: German Federal Ministry for Economic Affairs and Climate Action, NaLamKI project (Grant 01MK21003D); German Federal Ministry of Food and Agriculture (BMEL), LANDNETZ project (Grant 28DE101C18)
• Shared by: Fraunhofer HHI / Fraunhofer IVI (original data); this FiftyOne conversion shared by Harpreet Sahota
• Language(s): N/A (imagery dataset with English-language metadata)
• License: CC BY 4.0

Dataset Sources

• Repository: https://fraunhofer-hhi.github.io/AppleGrowthVision / https://datacloud.hhi.fraunhofer.de/s/KLFXDw9cWSzXk95
• Paper: AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards (arXiv:2505.14029)
• Demo: [More Information Needed]

Uses

Direct Use

• Apple fruit detection / object detection benchmarking (single class, apple), including combining this data with MinneApple and the Monastery Apple Dataset (MAD) as done in the source paper.
• BBCH principal growth-stage classification (bud development, leaf development, shoot development, inflorescence emergence, flowering, fruit development, fruit/seed maturity, senescence).
• Calibrated multi-view stereo reconstruction of orchard scenes (Brandenburg subset only, using the per-date camera calibration stored in dataset.info / the companion camera-rig visualization dataset).
• Yield-estimation research, using the num_apples detection counts and the 10-tree manual reference_apple_count ground truth as a small validation set.

Out-of-Scope Use

• The dataset is not exhaustively annotated: only 1,457 of the 21,407 images (~6.8%) have ground_truth apple bounding boxes, concentrated in BBCH principal stages 5-9 (fruit-visible stages) and specific capture dates/rows. It should not be treated as a fully-labeled detection set -- filter to the annotated or has-apples saved views (see below) for detection work.
• Not intended for tasks involving human subjects; this is strictly agricultural/orchard imagery.

Dataset Structure

This is a grouped FiftyOne dataset (media_type="image" in every slice) with 3 group slices, all populated from the same underlying sample collection:

Slice	Samples	Site	Description
left (default)	10,010	Brandenburg	Left camera of the calibrated stereo rig
right	10,010	Brandenburg	Right camera of the calibrated stereo rig
image	1,387	Saxony/Pillnitz	Handheld smartphone shots, one sample per group (singleton groups, reusing the group mechanism so both sites live in one dataset)

Total: 11,397 groups, 21,407 flat samples.

Fields

Field	FiftyOne type	Description
filepath	StringField	Path to the image (verbatim from source)
tags	ListField(StringField)	Provenance tags, e.g. brandenburg, saxony, 2023, 2024, pillnitz_2024_backup, row36-row47, labeled, has_apples
metadata	Metadata	Standard FiftyOne image metadata (size, mime type, dimensions)
group	Group	Group field (group.name is the slice: left/right/image)
site	StringField	brandenburg or saxony
subset	StringField	Finer-grained data-drop identifier: brandenburg, saxony_2023, saxony_2024, saxony_2024_counted_trees, saxony_pillnitz_row{36,37,38,39,40,44,45,46,47}
capture_date	StringField	Capture date (YYYY-MM-DD), set on Brandenburg samples from the per-date data folder
capture_datetime	StringField	Capture date + time (YYYYMMDD_HHMMSS), set on Saxony samples from the filename timestamp
shot_index	IntField	Brandenburg shot index within a capture date (shared by the L/R pair)
camera_hangle / camera_kappa0 / camera_aspect	FloatField	Per-camera calibration parameters (Brandenburg only), parsed from the date's calibration XML
calib_variant	StringField	"after" when a date had both a before/after calibration file (the "after" file is used as canonical)
row_index / tree_index	IntField	Orchard row and tree-within-row index (Saxony only)
picture_index	IntField	Picture number for a given tree/date (Saxony 2023/2024 main drops only)
is_detail	BooleanField	Whether the filename was flagged as a _detail (close-up) shot (Saxony 2023 only)
growth_stage	StringField	Free-text growth stage from the 2023 filenames: blossom, small_fruit, middle_fruit, fruit
bbch_code	StringField	Extended BBCH growth-stage code (e.g. "65", or "71/72" for one ambiguous table entry), derived from a site+date lookup table transcribed from the paper's Table 2
bbch_principal_stage	IntField	First digit of bbch_code (the BBCH principal stage, 0-9)
bbch_stage_name	StringField	Human-readable principal-stage name (e.g. "Flowering"), from the paper's Table 1
ground_truth	Detections	Apple bounding boxes (single class apple), converted from the source COCO annotations. Only present on samples that were part of the paper's dense-annotation pass; may be an empty Detections for reviewed-but-empty images (see below)
num_apples	IntField	len(ground_truth.detections), set alongside every ground_truth (derived convenience field for counting/sorting)
reference_apple_count	IntField	Manually verified (non-harvested) apple count for 10 specific Pillnitz trees, from the paper's reference-count table
reference_apple_count_bad_quality	IntField	Additional apples noted as "bad quality" in that same manual count, set alongside reference_apple_count (0 when not noted)

Label type

The only label type is Detections (ground_truth), a single class, apple, with relative [x, y, w, h] bounding boxes, plus per-box area and iscrowd carried over from the source COCO annotations. Bounding boxes (not polygons/masks) were used because that is the annotation format released with the source data.

labeled (in tags) marks every sample that appears in a source COCO file, including reviewed images with zero boxes (e.g. blossom-stage close-ups with no visible fruit). has_apples marks the subset of those with at least one box. This distinguishes "reviewed, no apples visible" from "never reviewed" (for the latter, ground_truth is simply unset).

dataset.info

• description, source (Fraunhofer HHI data-cloud share), paper (arXiv id)
• paper_reported_stats: the paper's headline numbers (9,317 stereo images, 1,125 densely annotated images, 31,084 apple labels) alongside a note that the actual imported counts differ somewhat (10,010 Brandenburg stereo pairs on disk; 1,457 densely-annotated images across both sites) -- documented rather than silently reconciled, since the discrepancy is in the released data itself, not in the FiftyOne conversion.
• bbch_scale: a short legend for the bbch_code / bbch_principal_stage taxonomy (Table 1 of the paper).

Saved views

The dataset ships with 7 saved views for common access patterns: brandenburg-stereo-pairs, saxony-images, annotated, has-apples, reference-count-trees, fruit-visible-stages (BBCH principal stages 5-9), and phenology-timeline (sorted by BBCH principal stage, then capture date).

Indexes

Indexes exist on site, subset, capture_date, growth_stage, bbch_code, bbch_principal_stage, bbch_stage_name, row_index, tree_index, num_apples, reference_apple_count, tags, plus compound indexes on (row_index, tree_index) and (site, bbch_principal_stage).

Not included in this repository

A companion 3D FiftyOne dataset (AppleGrowthVision-camera-rig) visualizes the Brandenburg stereo rig geometry as 16 .fo3d scenes (one per calibrated capture date), reconstructed from the calibration XML extrinsics. It is kept as a separate, small local dataset rather than a group slice here, since the rig geometry is per-date (16 configs), not per-image, and is not part of this Hub repository.

Dataset Creation

Curation Rationale

Existing apple-orchard datasets (MinneApple, the Washington State University robotic-harvesting dataset, the Michigan State O2RNet dataset, and the Monastery Apple Dataset) advanced fruit detection but did not capture growth stage or stereo/3D structure, and typically covered a limited window within a single season. AppleGrowthVision was curated to capture a complete phenological growth cycle with expert-validated BBCH stage labels and calibrated stereo imagery, enabling phenological analysis, fruit detection, and 3D orchard reconstruction from the same data.

Source Data

Data Collection and Processing

Two field sites were imaged:

• Brandenburg ("BB Obst"), Wesendahl, near Berlin: the same 33 Jonagold trees were imaged on 18 occasions throughout 2022-2023 using two calibrated Canon EOS 550D cameras (20mm lenses) triggered simultaneously, from front, left, right, and steep-angle views plus a circular sweep around the first tree in the row.
• Pillnitz orchard (LfULG Sachsen), near Dresden: rows ~70m long, trees spaced 1m apart in spindle form, 3m between rows. A subset of trees was photographed from multiple angles with a smartphone camera across 2023 (blossom through ripe fruit) and revisited in 2024 (single growth stage, after the orchard suffered fruit loss to late frost that year). Additional rows were covered by walking each row in one direction and back along the other side, capturing front/back views per tree.

Each Brandenburg capture date has its own camera calibration (extrinsics + intrinsics), stored as XML and used here to populate the per-sample camera_hangle/camera_kappa0/camera_aspect fields (and to build the separate camera-rig 3D visualization dataset).

Who are the source data producers?

Imagery was collected by the paper's authors and collaborators at Fraunhofer HHI and Fraunhofer IVI, at the BB Obst commercial orchard (Wesendahl) and the LfULG Sachsen research/education orchard (Pillnitz).

Annotations

Annotation process

Apple bounding-box annotation used an in-house annotation tool combining AI-assisted pre-labeling with human verification: a YOLOv8 model was first trained on a small manually-labeled seed set (108 non-stereo + 70 stereo images, combined with the full MinneApple dataset) and used to auto-annotate the remaining images. Annotations were only generated for images in BBCH principal stages 5-9 (inflorescence emergence through senescence), since earlier stages have no fruit to detect. Human annotators then reviewed and corrected the AI-generated boxes for 70 stereo images and 777 non-stereo images; final boxes are stored in Darknet/YOLO format and converted to COCO for release (and to FiftyOne Detections here).

Growth-stage labeling (the BBCH principal stage per capture date) was performed manually by an expert from LfULG Sachsen on a randomly-selected subset of images per date. The reference apple counts for 10 Pillnitz trees were obtained by manual visual counting in the field, without harvesting.

Who are the annotators?

Apple bounding boxes: the paper's authors/team, using the semi-automated AI-assisted pipeline described above. BBCH growth-stage labels: an agricultural expert from LfULG Sachsen (Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie). Reference apple counts: field staff at the Pillnitz orchard.

Personal and Sensitive Information

None. The dataset contains only orchard/tree/fruit imagery; no people or personal data are depicted or recorded.

Citation

BibTeX:

@article{vonhirschhausen2025applegrowthvision,
  title   = {AppleGrowthVision: A large-scale stereo dataset for phenological
             analysis, fruit detection, and 3D reconstruction in apple
             orchards},
  author  = {von Hirschhausen, Laura-Sophia and Magnusson, Jannes S. and
             Kovalenko, Mykyta and Boye, Fredrik and Rawat, Tanay and
             Eisert, Peter and Hilsmann, Anna and Pretzsch, Sebastian and
             Bosse, Sebastian},
  journal = {arXiv preprint arXiv:2505.14029},
  year    = {2025}
}

APA:

von Hirschhausen, L.-S., Magnusson, J. S., Kovalenko, M., Boye, F., Rawat, T., Eisert, P., Hilsmann, A., Pretzsch, S., & Bosse, S. (2025). AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards. arXiv preprint arXiv:2505.14029.

More Information

Extending MinneApple with AppleGrowthVision improved YOLOv8 F1-score by 7.69%, and adding it to MinneApple + MAD improved Faster R-CNN F1-score by 31.06% (paper Section 4.1). Six BBCH principal stages were predicted with over 95% accuracy using VGG16, ResNet152, DenseNet201, and MobileNetv2 (paper Section 4.2). The Brandenburg calibrated stereo imagery was also used for multi-view stereo orchard reconstruction via DISK features + LightGlue + COLMAP (paper Section 4.3); this repository does not include the reconstructed point clouds/meshes themselves, only the source imagery and calibration.

Dataset Card Authors

Harpreet Sahota

Dataset Card Contact

[More Information Needed]