README.md

---
annotations_creators:
- Alliance Bioversity & CIAT
- Producers Direct
task_categories:
- object-detection
size_categories:
- 10K<n<100K
pretty_name: Croppie coffee uganda
tags:
- yield estimates
- cherry count
- coffee cherries
- coffee trees
- arabica
- robusta
- digital agriculture
language:
- en
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.zip
  - split: val
    path: data/val.zip
license: cc-by-sa-4.0
license_link: https://www.gnu.org/licenses/quick-guide-gplv3.html
---
[Croppie](https://croppie.org/) © 2024 by [Producers Direct](https://producersdirect.org/) and [Alliance Bioversity & CIAT](https://alliancebioversityciat.org/) is licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)

**Funded by**: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) [Fair Forward Initiative - AI for All](https://huggingface.co/fair-forward)

# Croppie training datasets
## General information
Croppie dataset for machine-vision assisted coffee cherry detection. The dataset is made of a mix of Arabica and Robusta coffee tree parts (with and without a background isolation element) with individual bounding boxes around all coffee cherries. These RGB pictures were on-farm collected with smartphones with the collaboration of smallholder farmers. For instance, this dataset can be used for automated cherry count or coffee ripeness assessment.

The original dataset is composed of 633 images with about 61 050 unique bounding boxes over coffee cherries in YOLO format. This original dataset has been processed to cut-down all images into 480 x 640 size pieces and the full original image downscaled to 480 x 640. We provide the processed dataset with Python scripts that allow easy visualization of the annotated dataset.

Coffee cherries of more than 10mm (following the longitudinal axis) are annotated according to their color:
- green
- yellow
- red
- dark brown (overripe/dry cherries)
- an extra class indicates low visibility/unsure label appreciation.

Here is an example of an annotated image:
![plot](./images/annotated_1688033955437.jpg)

## Data structure
This repository has the following structure:
```
.
├── annotation_guide.html # original annotation instructions
├── classes.json  # json to convert numerical classes into the cherry type
├── data
│   ├── train.zip
│   └── val.zip
├── images
│   ├── annotated_1688033955437.jpg
│   ├── train_counts.png
│   └── val_counts.png
├── README.md
└── scripts  # script for easy visualization of the annotated data
    ├── label_training_images.py
    └── requirements.txt
```
### Dataset information
Each numerical class corresponds to the following cherry type:
```
{0: "dark_brown_cherry", 1: "green_cherry", 2: "low_visibility_unsure", 3: "red_cherry", 4: "yellow_cherry"}
```

* ```train```: 
  * Training dataset
  * 5 836 annotated images
  * YOLO format

![plot](./images/train_counts.png)

* ```val```: 
  * Validation dataset
  * 2 497 annotated images
  * YOLO format

![plot](./images/val_counts.png)

* ```annotation_guide.html```: instructions provided to label the images for cherry detection

## Scripts
The script ```label_training_images.py``` allows to label the images of the datasets and saves them in a folder  ```./_labelled_dataset_images```.
Assuming you are in the ```scripts``` folder, first
 run ```pip3 install -r requirements.txt``` if required package are not installed. After that, simply run 
```python3 label_training_images.py```

## License
[Croppie](https://croppie.org/) © 2024 by [Producers Direct](https://producersdirect.org/) and [Alliance Bioversity & CIAT](https://alliancebioversityciat.org/) is licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)

## Funding

**Funded by**: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) [Fair Forward Initiative - AI for All](https://huggingface.co/fair-forward)