Flesh out dataset card with paper details, structure, and citation

README.md

@@ -1,12 +1,16 @@
 ---
-annotations_creators: []
-language: en
+annotations_creators:
+- expert-generated
+language:
+- en
 license: cc-by-4.0
 size_categories:
 - n<1K
-task_categories: []
+task_categories:
+- image-segmentation
+- object-detection
 task_ids: []
-pretty_name: vineyard-pruning
+pretty_name: Vineyard Dataset for Pruning
 tags:
 - agriculture
 - fiftyone
@@ -21,11 +25,9 @@ description: 536 vineyard images (Tierra de Barros, Badajoz, Spain) with polygon
 dataset_summary: '
 
 
-
   ![image/png](dataset_preview.jpg)
 
 
-
   This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 536 samples.
 
 
@@ -65,19 +67,14 @@ dataset_summary: '
 
   ```
 
+
   '
 ---
 
-# Dataset Card for vineyard-pruning
-
-<!-- Provide a quick summary of the dataset. -->
-
-
-
+# Dataset Card for Vineyard Dataset for Pruning
 
 ![image/png](dataset_preview.jpg)
 
-
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 536 samples.
 
 ## Installation
@@ -102,135 +99,128 @@ dataset = load_from_hub("harpreetsahota/vineyard-pruning")
 session = fo.launch_app(dataset)
 ```
 
-
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
+This dataset contains 536 vineyard images collected in Tierra de Barros, in the southwestern province of Badajoz, Extremadura, Spain, on *Vitis vinifera* vines of the "pardina"/"parda" and "macabeo"/"viura"/"lardot" varieties, grown in hedgerows under the "Ribera del Guadiana" designation of origin. Images were captured during manual pruning, performed by qualified professionals, under natural lighting and from varied angles, using a Canon EOS 2000D DSLR and a Samsung SM-A715F smartphone. Each image is annotated with polygon masks for three vine parts -- **trunk**, **unpruned shoot**, and **pruned shoot** -- labeled with VGG Image Annotator (VIA). The dataset has previously been used to train Mask R-CNN models for automatic localization of vine parts (reported mAP50 of 60.08), in support of automated/robotic vineyard pruning systems.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** cc-by-4.0
+- **Curated by:** Elia Pacioni, Eugenio Abengozar-García, Miguel Macías Macías, Carlos J. García Orellana, Horacio M. González Velasco, and Antonio García Manso (Universidad de Extremadura)
+- **Funded by:** Ministerio de Ciencia, Innovación y Universidades, Spain (Grant TED2021-131242B-I00, "Sistema robotizado para la poda inteligente de viñedos")
+- **Shared by:** Original data shared by the curators via Mendeley Data; this FiftyOne-formatted version prepared and shared by harpreetsahota
+- **Language(s):** Not applicable (image dataset; documentation/labels are in English)
+- **License:** CC BY 4.0
 
-### Dataset Sources [optional]
+### Dataset Sources
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Vineyard dataset for pruning, Mendeley Data, V2](https://data.mendeley.com/datasets/n8cs4ns97p/2) (DOI: 10.17632/n8cs4ns97p.2)
+- **Paper:** Pacioni, E., Abengózar, E., Macías Macías, M., García Orellana, C.J., González Velasco, H.M., & García Manso, A. (2025). Vineyard dataset for automatic pruning based on main parts localization. *Data in Brief*, 59, 111335. https://doi.org/10.1016/j.dib.2025.111335
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+This dataset is suitable for training and evaluating object detection / instance segmentation models (e.g. Mask R-CNN, or other polygon/mask-based detectors) to localize vine trunks and shoots for automated pruning robotics, viticulture research, and precision agriculture applications. It can also support studies of pruning-stage classification (distinguishing already-pruned from not-yet-pruned canes) given the per-image `num_shoots` / `num_pruned_shoots` counts included in this FiftyOne version.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+The dataset is limited to two grape varieties ("pardina" and "macabeo") grown in a single region (Tierra de Barros, Badajoz, Spain) under hedgerow training, so models trained on it may not generalize to other varieties, trellising systems, or geographies without further validation. It contains no disease, pest, or fruit/cluster annotations, and is not suitable for tasks outside vine-part localization (e.g. yield estimation, disease detection).
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+This is a **flat image dataset** (`media_type="image"`) with 536 samples and no train/val/test splits (the source distributes a single unsplit pool).
 
-[More Information Needed]
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `filepath` | `StringField` | Absolute path to the source JPEG image |
+| `metadata` | `ImageMetadata` | Auto-computed width, height, MIME type, and file size |
+| `ground_truth` | `Polylines` | One closed, filled `Polyline` per annotated vine-part instance, `label` in `{"shoot", "pruned shoot", "trunk"}` |
+| `num_shoots` | `IntField` | Count of `"shoot"` (unpruned bud-stub) polylines in the image (derived) |
+| `num_pruned_shoots` | `IntField` | Count of `"pruned shoot"` polylines in the image (derived) |
+| `num_trunks` | `IntField` | Count of `"trunk"` polylines in the image (derived) |
+| `num_regions` | `IntField` | Total polyline count for the image (derived) |
+| `camera_make` | `StringField` | EXIF camera manufacturer, when available (412/536 images; 124 have no EXIF) |
+| `camera_model` | `StringField` | EXIF camera model, when available |
+| `capture_date` | `StringField` | ISO-8601 EXIF `DateTimeOriginal`, when available |
+| `resolution` | `StringField` | `"{width}x{height}"` convenience string (5 distinct resolutions, 800x600 to 6000x4000) |
+
+**`dataset.info`** stores the original source URL, paper citation, license, a short description, and the `classes` list (`["shoot", "pruned shoot", "trunk"]`).
+
+**Label type choice:** the source annotations are polygon masks (VIA `shape_attributes.name == "polygon"`), so each region maps to a FiftyOne `Polyline` (`closed=True`, `filled=True`) rather than a bounding-box `Detection`, preserving the original vertex-level segmentation boundary.
+
+**Parsing decisions:**
+- VIA stores absolute pixel coordinates (`all_points_x`/`all_points_y`); these were converted to FiftyOne's normalized `[0, 1]` `points` using each image's actual on-disk width/height (the VIA JSON's `size` field is the file's *byte size*, not pixel dimensions, and was not used for this).
+- The VIA class attribute (`region_attributes.clase`, a Spanish-named radio field) maps `"1"→"shoot"`, `"2"→"pruned shoot"`, `"3"→"trunk"`.
+- Samples were linked to their VIA record via the `filename` field inside each record, not the outer VIA dict key (which is an internal VIA dedup key concatenating the filename with part of a timestamp).
+- 6,968 total polygon regions across 536 images (5,329 shoot / 887 pruned shoot / 752 trunk) -- 100% of images have at least one annotation (min 2, avg 13, max 30 regions/image).
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+Manual pruning is a labor-intensive, seasonal viticulture task, and labor scarcity is a recognized challenge for the Spanish agricultural sector. This dataset was curated to support development of an automated/robotic vine-pruning system by providing labeled imagery of the plant parts (trunk, unpruned shoot, pruned shoot) that such a system must localize.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
+Images were collected in vineyards in Tierra de Barros, Badajoz, Extremadura, Spain (approx. 38.693, -6.409), on 10-20 year old, irrigated, hedgerow-trained *Vitis vinifera* vines ("pardina" and "macabeo" varieties) under the "Ribera del Guadiana" designation of origin. Photos were taken during manual pruning under natural lighting from varied angles, using a Canon EOS 2000D (34mm lens, sRGB, f/1.8) and a Samsung SM-A715F smartphone, producing five distinct resolutions from 800x600 to 6000x4000. The source paper states images were collected across 2021-2023; EXIF `DateTimeOriginal` values recovered during FiftyOne ingestion show only 2022 (Samsung batch, 261 images) and 2024 (Canon batch, 151 images), with 124 images (the lower-resolution buckets) carrying no EXIF data at all -- noted here for transparency, as it was not resolved from the source materials.
 
 #### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+Images were captured by the curating research team (Universidad de Extremadura, Instituto de Computación Científica Avanzada) during pruning work carried out by qualified vineyard pruning professionals.
 
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
+Each image was manually labeled with polygon masks using VGG Image Annotator (VIA). Three region classes were defined: **trunk** (the vine trunk, present in nearly every image), **unpruned shoot** (only the first bud-bearing segment of an as-yet-unpruned cane is labeled, not its full length), and **pruned shoot** (shoots that have already been cut). Per the source paper, label accuracy was validated with ad-hoc scripts in both VIA and COCO format, though only the VIA project file is distributed with the dataset.
 
 #### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+The dataset curators (Universidad de Extremadura team listed under Curated By).
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+None identified. Images depict vineyard plant material (trunks and shoots); no people, faces, or other personally identifying information are annotated or apparent in the sampled imagery.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{pacioni2025vineyard,
+  title   = {Vineyard dataset for automatic pruning based on main parts localization},
+  author  = {Pacioni, Elia and Abeng{\'o}zar, Eugenio and Mac{\'i}as Mac{\'i}as, Miguel and Garc{\'i}a Orellana, Carlos J. and Gonz{\'a}lez Velasco, Horacio M. and Garc{\'i}a Manso, Antonio},
+  journal = {Data in Brief},
+  volume  = {59},
+  pages   = {111335},
+  year    = {2025},
+  doi     = {10.1016/j.dib.2025.111335}
+}
+
+@misc{pacioni2024vineyarddata,
+  title     = {Vineyard dataset for pruning},
+  author    = {Pacioni, Elia and Abengozar-Garc{\'i}a, Eugenio and Mac{\'i}as Mac{\'i}as, Miguel and Garc{\'i}a Orellana, Carlos J. and Gonz{\'a}lez Velasco, Horacio M. and Garc{\'i}a Manso, Antonio},
+  year      = {2024},
+  publisher = {Mendeley Data},
+  version   = {V2},
+  doi       = {10.17632/n8cs4ns97p.2}
+}
+```
 
 **APA:**
 
-[More Information Needed]
+Pacioni, E., Abengózar, E., Macías Macías, M., García Orellana, C. J., González Velasco, H. M., & García Manso, A. (2025). Vineyard dataset for automatic pruning based on main parts localization. *Data in Brief*, 59, 111335. https://doi.org/10.1016/j.dib.2025.111335
 
-## Glossary [optional]
+## More Information
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+This FiftyOne-formatted version was produced by parsing the source VIA (`vidprune.json`) project file directly, since VIA is not one of FiftyOne's built-in importer formats. All 536 images and 6,968 polygon regions were verified to match the counts reported in the source paper's Table 1 and Figure 2 during ingestion.
 
-[More Information Needed]
+## Dataset Card Authors
 
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+harpreetsahota
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[More Information Needed]