This is a detailed description of the dataset, a data sheet for the dataset as proposed by Gebru et al.
Embrapa ADD 256 (Apples by Drones Detection Dataset — 256 × 256) was created to provide images and annotation for research on *apple detection in orchards for UAV-based monitoring in apple production.
Apple detection in low-resolution scenarios, similar to the aerial images employed here.
The building of the ADD256 dataset was supported by the Embrapa SEG Project 01.14.09.001.05.04, Image-based metrology for Precision Agriculture and Phenotyping, and FAPESP under grant (2017/19282-7).
Each instance consists of an RGB image and an annotation describing apples locations as circular markers (i.e., presenting center and radius).
The dataset consists of 1,139 images containing 2,471 apples.
Each instance contains an 8-bits RGB image. Its corresponding annotation is found in the JSON files: each apple marker is composed by its center (cx, cy) and its radius (in pixels), as seen below:
"gebler-003-06.jpg": [
{
"cx": 116,
"cy": 117,
"r": 10
},
{
"cx": 134,
"cy": 113,
"r": 10
},
{
"cx": 221,
"cy": 95,
"r": 11
},
{
"cx": 206,
"cy": 61,
"r": 11
},
{
"cx": 92,
"cy": 1,
"r": 10
}
],
Dataset.ipynb is a Jupyter Notebook presenting a code example for reading
the data as a PyTorch's Dataset (it should be straightforward to adapt the code
for other frameworks as Keras/TensorFlow, fastai/PyTorch, Scikit-learn, etc.)
Everything is included in the dataset.
The dataset comes with specified train/test splits. The splits are found in lists stored as JSON files.
| Number of images | Number of annotated apples | |
|---|---|---|
| Training | 1,025 | 2,204 |
| Test | 114 | 267 |
| Total | 1,139 | 2,471 |
Dataset recommended split.
Standard measures from the information retrieval and computer vision literature should be employed: precision and recall, F1-score and average precision as seen in COCO and Pascal VOC.
The first experiments run on this dataset are described in A methodology for detection and localization of fruits in apples orchards from aerial images by Santos & Gebler (2021), DOI 10.5753/sbiagro.2021.18369.
The data employed in the development of the methodology came from two plots located at the Embrapa’s Temperate Climate Fruit Growing Experimental Station at Vacaria-RS (28°30’58.2”S, 50°52’52.2”W). Plants of the varieties Fuji and Gala are present in the dataset, in equal proportions. The images were taken during December 13, 2018, by an UAV (DJI Phantom 4 Pro) that flew over the rows of the field at a height of 12 m. The images mix nadir and non-nadir views, allowing a more extensive view of the canopies. A subset from the images was random selected and 256 × 256 pixels patches were extracted.
T. T. Santos and L. Gebler captured the images in field. T. T. Santos performed the annotation.
The circular markers were annotated using the VGG Image Annotator (VIA).
WARNING: Find non-ripe apples in low-resolution images of orchards is a challenging task even for humans. ADD256 was annotated by a single annotator. So, users of this dataset should consider it a noisy dataset.
No preprocessing was applied.
The dataset is available at GitHub.
The dataset was released in October 2021.
The data is released under Creative Commons BY-NC 4.0 (Attribution-NonCommercial 4.0 International license). There is a request to cite the corresponding paper if the dataset is used. For commercial use, contact Embrapa Agricultural Informatics business office.
There are no fees or restrictions. For commercial use, contact Embrapa Agricultural Informatics business office.
The dataset is hosted at Embrapa Agricultural Informatics and all comments or requests can be sent to Thiago T. Santos (maintainer).
There is no scheduled updates.
Contributors should contact the maintainer by e-mail.
The maintainers and their institutions are exempt from any liability, judicial or extrajudicial, for any losses or damages arising from the use of the data contained in the image database.