Embrapa ADD 256

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 This is a detailed description of the dataset, a data sheet for the dataset as proposed by Gebru *et al.* Motivation for Dataset Creation ------------------------------- ### Why was the dataset created? 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. ### What (other) tasks could the dataset be used for? Apple detection in *low-resolution scenarios*, similar to the aerial images employed here. ### Who funded the creation of the dataset? 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). Dataset Composition ------------------- ### What are the instances? Each instance consists of an RGB image and an annotation describing apples locations as _circular markers_ (i.e., presenting center and radius). ### How many instances of each type are there? The dataset consists of 1,139 images containing 2,471 apples. ### What data does each instance consist of? 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.) ### Is everything included or does the data rely on external resources? Everything is included in the dataset. ### Are there recommended data splits or evaluation measures? 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. ### What experiments were initially run on this dataset? The first experiments run on this dataset are described in *A methodology for detection and location of fruits in apples orchards from aerial images* by Santos & Gebler (2021). Data Collection Process ----------------------- ### How was the data collected? 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. ### Who was involved in the data collection process? T. T. Santos and L. Gebler captured the images in field. T. T. Santos performed the annotation. ### How was the data associated with each instance acquired? 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*. Data Preprocessing ------------------ ### What preprocessing/cleaning was done? No preprocessing was applied. Dataset Distribution -------------------- ### How is the dataset distributed? The dataset is available at GitHub. ### When will the dataset be released/first distributed? The dataset was released in October 2021. ### What license (if any) is it distributed under? 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. ### Are there any fees or access/export restrictions? There are no fees or restrictions. For commercial use, contact Embrapa Agricultural Informatics business office. Dataset Maintenance ------------------- ### Who is supporting/hosting/maintaining the dataset? The dataset is hosted at Embrapa Agricultural Informatics and all comments or requests can be sent to Thiago T. Santos (maintainer). ### Will the dataset be updated? There is no scheduled updates. ### If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? Contributors should contact the maintainer by e-mail. ### No warranty 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*.