| | --- |
| | task_categories: |
| | - image-to-image |
| | - image-feature-extraction |
| | - object-detection |
| | language: |
| | - en |
| | tags: |
| | - plant |
| | - precision agriculture |
| | - plant phenotyping |
| | - tracking |
| | size_categories: |
| | - 10B<n<100B |
| | pretty_name: CanolaTrack |
| | --- |
| | |
| | # CanolaTrack |
| |
|
| | **CanolaTrack** is a curated dataset for **leaf-level multi-object tracking (MOT)** and **detection** from top-down RGB imagery of *Brassica napus* (canola) plants. Each sequence records a single plant over time; frames contain annotated **bounding boxes** with **persistent leaf IDs** for tracking. |
| |
|
| | - For baseline methods and a reference pipeline built on CanolaTrack, see **LeafTrackNet** (training, inference, and TrackEval integration) in our [Github repo](https://github.com/shl-shawn/LeafTrackNet). |
| |
|
| | --- |
| |
|
| | ## Dataset Summary |
| |
|
| | - **Domain:** Plant phenotyping (leaf-level analysis, time series) |
| | - **Modalities:** RGB images (top-down) |
| | - **Use cases:** Multi-object tracking (leaf IDs), detection, re-identification |
| | - **Content:** Sequences of a single plant over days; each frame has MOT-style annotations |
| | - **Annotations:** `gt/gt.txt` per sequence with **frame**, **leaf_id**, **x**, **y**, **w**, **h** (pixels) |
| | - **Extras:** YOLOv10 **proposals JSONs** and **LeafTrackNet model weights**for reproducible tracking baselines |
| | |
| | --- |
| | |
| | ## Repository Structure |
| | ``` |
| | CanolaTrack/ |
| | │ ├── train/ |
| | │ │ └── <plant_id>/ |
| | │ │ ├── gt/gt.txt # CSV: frame,id,x,y,w,h,,,* |
| | │ │ └── img/{frame:08d}.jpg |
| | │ └──val/ |
| | │ └── <plant_id>/ |
| | │ ├── gt/gt.txt |
| | │ └── img/{frame:08d}.jpg |
| | proposals/ # detection proposals for standardized benchmarking |
| | │ ├── det_db_train.json |
| | │ └── det_db_val.json |
| | weights/ # detctors and tracker weights |
| | └── <files> |
| | ``` |
| | |
| | ## Supported Tasks and Benchmarks |
| | |
| | - **Multi-Object Tracking (MOT)** at the **leaf** level |
| | - **Object Detection** (per-frame leaf boxes) |
| | - **Leaf Segmentation** (per-frame leaf masks) |
| | |
| | --- |
| | |
| | ## How to Cite |
| | Please cite the dataset and the accompanying papers: |
| | |
| | ```bib |
| | @article{leaftracknet2025, |
| | title={LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping}, |
| | year={2025}, |
| | author = {}, |
| | url = {} |
| | } |
| | ``` |
| | |
| | > CanolaTrack dataset© BASF SE 2025. This dataset may be freely used for non-commercial research and educational purposes. |
| | |
| | |
