ACFR Orchard Fruit Dataset

Fruit Dataset

The dataset - acfr-multifruit-2016 - contains images and annotations for different fruits, collected at different farms across Australia.
Download Link: acfr-multifruit-2016

The dataset was gathered by the agriculture team at the Australian Centre for Field Robotics, The University of Sydney, Australia. Further information about the group can be found at sydney.edu.au/acfr/agriculture

Data summary

Fruit Image Info Annotation Info Notes
Apples 1120 Images
PNG Image Data
308 x 202, 8-bit/color RGB
Sensor: PointGrey Ladybug3
Circle Annotation (x,y,radius)
Pixel-wise annotation
Collected at Warburton, Australia
Apple varieties: Pink Lady, Kanzi
Mangoes 1964 Images
PNG Image Data
500 x 500, 16-bit/color RGB
Sensor: Prosilica GT3300c
Strobes Used
Rectangle Annotation (x,y,dx,dy) Collected at Bundaberg, Australia
Mango varieties: Calypso
Almonds 620 Images
PNG Image Data
308 x 202, 8-bit/color RGB
Sensor: Canon EOS60D
Rectangle Annotation (x,y,dx,dy) Collected at Mildura, Australia
Almond variety: Nonpareil

Dataset Structure:

acfr-fruit-dataset
├── almonds
│   ├── annotations
│   ├── images
│   ├── labelmap.json
│   └── sets
│       ├── all.txt
│       ├── test.txt
│       ├── train.txt
│       ├── train_val.txt
│       └── val.txt
├── apples
│   ├── annotations
│   ├── images
│   ├── labelmap.json
│   ├── segmentations
│   └── sets
│       ├── all.txt
│       ├── test.txt
│       ├── train.txt
│       ├── train_val.txt
│       └── val.txt
├── mangoes
│   ├── annotations
│   ├── images
│   ├── labelmap.json
│   └── sets
│       ├── all.txt
│       ├── test.txt
│       ├── train.txt
│       ├── train_val.txt
│       └── val.txt
└── readme.txt
		

Results

The fruit detection F1-scores as computed in [1] are:
Fruit F1-score
Apple 0.904
Mango 0.908
Almond 0.775
Refer to [1] for specifics on evaluation procedure.

[1] Bargoti, S., & Underwood, J. (2016). Deep Fruit Detection in Orchards. arXiv preprint arXiv:1610.03677. [Submitted to ICRA (2017)]

Annotation Info:

All object annotations were captured using PychetLabeller, which can also be used to quickly visualise the annotations (see code help page).

Both Almond and Mango datasets are annotated with rectangles, whereas the apples, being circular in nature, are annotated using Circles. The apple dataset contains additional pixel-level annotations.

To run the labeller:
python -m pychetlabeller apples/images/ apples/annotations/ --tool circle --labelmap apples/labelmap.json
python -m pychetlabeller mangoes/images/ mangoes/annotations/ --tool rectangle --labelmap mangoes/labelmap.json
python -m pychetlabeller almonds/images/ almonds/annotations/ --tool rectangle --labelmap almonds/labelmap.json

Citation

The dataset presented here has been used in multiple studies conducted at ACFR.
When using this dataset in your research, please cite the following papers.

For fruit level annotations:
@article{bargoti2016deep,
  title={Deep Fruit Detection in Orchards},
  author={Bargoti, Suchet and Underwood, James},
  journal={arXiv preprint arXiv:1610.03677},
  year={2016}
}

For pixel level segmentations (apple only):
@article{Bargoti2016,
  author={Bargoti, Suchet and Underwood, James},
  journal={To Appear in Journal of Field Robotics},
  title={Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards},
  year={2016}
}

Other Citations

Collection of all papers that have used part/all of the above data.

Fruit Annotations: Pixel-level annotations (apples only):

Dataset Examples


Apple annotations have been converted to square bounding-boxes for visualisation.

Platform

The apple and mango dataset were collected using Shrimp - an unmanned ground vehicle, built at the Australian Centre for Field Robotics, The University of Sydney.
Figure below shows the vechicle operating in the apple and mango orchards.

Contact

For any questions, feel free to contact: