--- license: other license_name: cropandweed-non-commercial license_link: https://github.com/cropandweed/cropandweed-dataset/blob/main/LICENCE task_categories: - object-detection - image-segmentation tags: - fiftyone - image - object-detection - image-segmentation - keypoint-detection - agriculture - precision-agriculture - crop-and-weed - plants - fine-grained-classification size_categories: - 1K This is a FiftyOne dataset with 8,034 samples, converted from the official CropAndWeed dataset (WACV 2023) release. It contains high-resolution top-down field images annotated with 100 fine-grained crop/weed species-and-growth-stage classes, provided as 2D bounding boxes, stem keypoints, and per-pixel semantic segmentation masks, along with per-image environmental meta-annotations (moisture, soil, lighting, separability). --- # Dataset Card for CropAndWeed This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8,034 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh dataset = fouh.load_from_hub("harpreetsahota/CropAndWeed") session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description The CropAndWeed dataset is a large-scale, high-variability image dataset for Precision Agriculture, targeting the fine-grained identification of crop and weed species to support automated, plant-specific weed intervention. It provides bounding boxes, stem positions, and semantic segmentation masks for about 112k plant instances across more than 8k high-resolution real-world images, along with rich per-image meta-annotations describing recording conditions. Images come from two complementary sources: an **Application Set** of images captured at several hundred commercially used agricultural sites in Austria (realistic, uncontrolled field conditions), and an **Experimental Set** captured on dedicated experimental plots specifically cultivated to enrich the dataset with otherwise underrepresented, rare weed species. - **Curated by:** Daniel Steininger, Andreas Trondl, Gerardus Croonen, Julia Simon, and Verena Widhalm, AIT Austrian Institute of Technology - **Funded by:** Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, and the Austrian Research Promotion Agency (FFG), under the "ICT of the Future" research project HARV.EST (FFG No. 892347) - **Shared by:** AIT Austrian Institute of Technology (original release); this FiftyOne format conversion was prepared by Harpreet Sahota - **Language(s):** N/A (imagery, no text) - **License:** Custom non-commercial research license (see [Dataset Sources](#dataset-sources) and [More Information](#more-information)) ### Dataset Sources - **Repository:** https://github.com/cropandweed/cropandweed-dataset - **Paper:** [The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation](https://openaccess.thecvf.com/content/WACV2023/papers/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.pdf) (WACV 2023) - **Demo:** [More Information Needed] ## Uses ### Direct Use - Training and benchmarking object detection, fine-grained plant classification, semantic/ panoptic segmentation, and stem/anchor-point (keypoint) localization models for Precision Agriculture and automated crop/weed manipulation. - Academic research, teaching, and personal experimentation into robust, high-variability agricultural computer vision, including studying how incorporating multiple weed species into training affects crop-detection accuracy. - Analysis of model robustness to environmental conditions using the per-image moisture, soil, lighting, and separability meta-annotations. ### Out-of-Scope Use - Any commercial use, including licensing, selling, or otherwise using the data (or the raw data itself in redistributed form) for commercial gain — explicitly disallowed by the dataset license. - Redistributing the dataset or modified versions of it. Derivative works that are abstract representations not allowing recovery of the original data (e.g. trained model weights) are permitted; redistributing the images/annotations themselves is not. - Use as a certified or standalone agronomic/regulatory plant-identification tool without expert validation — annotations were produced for computer-vision benchmarking purposes. ## Dataset Structure This is a flat (non-grouped) image dataset with `media_type="image"` and 8,034 samples, one sample per annotated image (1920x1088 RGB JPEG). It was parsed from the official raw annotation release (`bboxes/`, `labelIds/CropAndWeed/`, `params/`), preserving every modality shipped by the dataset authors. | Field | FiftyOne type | Description | |-------|---------------|-------------| | `filepath` | `StringField` | Absolute path to the source RGB image | | `metadata` | `ImageMetadata` | Standard image metadata (`width`, `height`, `size_bytes`, `mime_type`, `num_channels`) | | `recording_set` | `StringField` | `"Application Set"` or `"Experimental Set"`, parsed from the `ave-`/`vwg-` filename prefix | | `session` | `StringField` | 4-digit recording-session identifier (verbatim from filename) | | `image_id` | `StringField` | 4-digit image identifier within the session (verbatim from filename) | | `ground_truth` | `Detections` | One `Detection` per annotated plant instance (2D bounding box), `label` is one of 100 fine-grained species/growth-stage classes (or the `Vegetation` fallback class) | | `stem_points` | `Keypoints` | One `Keypoint` per annotated instance marking its stem (root) position | | `segmentation` | `Segmentation` | Per-pixel semantic mask referencing a single-channel PNG (`mask_path`), same 100 classes as `ground_truth` | | `moisture` / `moisture_id` | `StringField` / `IntField` | Soil moisture at capture time: `dry`, `medium`, or `wet` (raw id 0-2) | | `soil` / `soil_id` | `StringField` / `IntField` | Soil granularity: `fine`, `medium`, or `coarse` (raw id 0-2) | | `lighting` / `lighting_id` | `StringField` / `IntField` | Lighting condition: `sunny` or `diffuse` (raw id 0-1) | | `separability` / `separability_id` | `StringField` / `IntField` | Subjective difficulty of separating plants from soil/each other: `easy`, `medium`, or `hard` (raw id 0-2) | **Label types and why:** - **`ground_truth` (`Detections`)** — each row of the source `bboxes/*.csv` files (`Left, Top, Right, Bottom, LabelID, StemX, StemY`) is converted to a `Detection` with a relative `[x, y, w, h]` bounding box (required by FiftyOne). Each `Detection` also carries `label_id` (the raw numeric class id, 0-99 or 255) and `stem_x`/`stem_y` (the same stem position, kept as absolute pixel coordinates for reference). - **`stem_points` (`Keypoints`)** — the dataset authors treat stem/anchor-point localization as a first-class learning task in its own right (Section 5.3 of the paper), so the stem position accompanying every bounding-box row is *also* materialized as a normalized-coordinate `Keypoint`, letting it be visualized and queried independently of `ground_truth`. - **`segmentation` (`Segmentation`)** — the `labelIds/CropAndWeed/*.png` semantic masks are referenced via `mask_path` (not embedded as in-database arrays) to avoid duplicating ~8k full-resolution masks in the database. `dataset.mask_targets["segmentation"]` maps all 101 raw pixel values (0-99 plus 255) to their class names. - **Primitive fields for `params`** — the four categorical per-image parameters are stored both as a human-readable string (`moisture`, `soil`, `lighting`, `separability`) and the raw numeric id shipped in `params/*.csv` (`moisture_id`, etc.), since the string versions are more convenient for filtering/grouping in the FiftyOne App. **`dataset.classes`** — `ground_truth` and `stem_points` are both registered with the same 100-class list (99 fine-grained species/growth-stage labels plus the `Vegetation` fallback). **`dataset.info`** contains the source repository URL, paper citation, and a short license summary. **Parsing decisions:** - Only the base, 100-class `CropAndWeed` label specification was imported. The official repository also defines simplified variants (`Fine24`, `CropsOrWeed9`, `CropOrWeed2`, `Maize1`/`Maize2`, `SugarBeet1`/`SugarBeet2`, etc.), which are pure label-ID remappings of these same raw annotations with no additional information — equivalent groupings can be reconstructed from `ground_truth.detections.label_id` using FiftyOne label-mapping views if needed, without re-importing. - 234 of the 8,034 images have no annotated plant instances (their source bounding-box CSVs are empty); these samples have no `ground_truth`/`stem_points` but still have a `segmentation` mask (all background/`Soil`). All 8,034 images have a `segmentation` mask. - `label_id` `255` (`Vegetation`) is a fallback class for tiny (<16² px), excluded, or visually ambiguous instances, and appears in both `ground_truth` and `segmentation`. - No official train/val/test split is distributed with the raw annotation release (the paper's 70:15:15 split was generated for the authors' internal benchmarking only and is not shipped in the public download), so no split/tag field is set on this dataset. ## Dataset Creation ### Curation Rationale Precision Agriculture approaches to automated weed intervention require large amounts of highly variable, annotated training data, which the authors found lacking in prior work (most existing datasets are small, laboratory-controlled, provide only one annotation modality, or cover far fewer species). CropAndWeed was curated to close this gap: a large-scale, high-variability dataset spanning 74 crop and weed species (16 crop, 58 weed), with multi-modal annotations (bounding boxes, semantic masks, and stem positions) rarely provided together elsewhere, and an emphasis on realistic field conditions plus specially cultivated plots for rare species. ### Source Data #### Data Collection and Processing Images were captured over a period of four years (March-July) using a semi-professional full-frame SLR camera with a constant 50mm focal length, in auto-exposure mode, from an approximate top-down perspective at a height of about 1.1 meters. Each capturing session corresponds to a unique agricultural site or experimental plot at a specific point in time, yielding an average of about 20 images per session at intervals of at least three meters to avoid redundancy. | | Sessions (recorded / annotated) | Images (recorded / annotated) | |---|---|---| | Experimental Set | 1,363 / 665 | 22,597 / 4,990 | | Application Set | 738 / 264 | 21,217 / 3,044 | | **Total** | **2,101 / 929** | **43,814 / 8,034** | The 8,034 annotated images contain 111,953 annotated plant instances in total. Each recording session is also enriched with the `moisture`, `soil`, `lighting`, and `separability` meta-annotations included in this dataset, plus timestamps, GPS coordinates, and camera parameters recorded by the authors (not part of this FiftyOne conversion). #### Who are the source data producers? The AIT Austrian Institute of Technology research team (Daniel Steininger, Andreas Trondl, Gerardus Croonen, Julia Simon, Verena Widhalm), in cooperation with the experimental farm Groß-Enzersdorf, produced and recorded the source imagery. ### Annotations #### Annotation process To increase annotation throughput, each image was first automatically pre-segmented into soil vs. vegetation pixels (initially via Otsu color thresholding, later via a CNN trained on already-annotated data). Annotators refined these masks and assigned the target species class. Bounding boxes were then auto-generated from the refined mask and manually corrected, and each plant's stem position was manually annotated. Image batches with high ambiguity or a high number of plant instances were reviewed by voting among multiple annotators as an additional quality-control step. #### Who are the annotators? The AIT annotation team, credited in the paper as Gulnar Bakytzhan, Vanessa Klugsberger, and Marlene Glawischnig, with additional support from Caroline Huber, Reinhard Neugschwandtner, and Helmut Wagentristl at the experimental farm Groß-Enzersdorf. #### Personal and Sensitive Information None. The dataset consists exclusively of top-down agricultural field imagery (soil, crops, and weeds); it contains no people, faces, or other personal or sensitive information. ## Citation **BibTeX:** ```bibtex @InProceedings{Steininger_2023_WACV, author = {Steininger, Daniel and Trondl, Andreas and Croonen, Gerardus and Simon, Julia and Widhalm, Verena}, title = {The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {3729-3738} } ``` **APA:** Steininger, D., Trondl, A., Croonen, G., Simon, J., & Widhalm, V. (2023). The CropAndWeed dataset: A multi-modal learning approach for efficient crop and weed manipulation. In *Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)* (pp. 3729-3738). ## More Information The dataset is released by AIT Austrian Institute of Technology to academic and non-academic entities for non-commercial purposes only (academic research, teaching, scientific publications, or personal experimentation). Redistribution of the dataset or modified versions of it is not permitted; derivative works that are abstract representations not allowing recovery of the original data (e.g. trained models) are permitted. See the [official license](https://github.com/cropandweed/cropandweed-dataset/blob/main/LICENCE) for the full terms. The official repository additionally provides scripts to remap the raw 100-class annotations into 20 predefined simplified dataset variants (e.g. `Fine24`, `CropsOrWeed9`, `CropOrWeed2`) for applications that don't require the full fine-grained species/growth-stage taxonomy. ## Dataset Card Authors Harpreet Sahota (FiftyOne conversion and dataset card) ## Dataset Card Contact [More Information Needed]