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Peer-reviewedCC BY-NCData in brief

Synthetic Apple Collection (SyntAC): render-based synthetic datasets for apple detection.

  • Jhonny Hueller1,2
  • Massimo Vecchio1
  • Fabio Antonelli1
  1. 1Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.
  2. 2University of Trento, Via Sommarive 9, Trento, 38123 Italy.
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Abstract

We present the Synthetic Apple Collection (SyntAC), a 20 GB set of synthetic images developed for apple detection in RGB images. The collection consists of 3 datasets, each containing 3840 image samples, for a total of 11,520 images, generated using Unreal Engine 5 and annotated in the YOLO format. The datasets include a variety of scenes in a virtual environment representing an apple orchard in different illumination conditions. Annotations are produced procedurally from engine data, providing perfect, pixel-to-pixel labeling and ensuring full reproducibility of the annotation process. The collection is designed to be easily reusable for the development, testing, and benchmarking of object detection algorithms and, more generally, machine learning models for precision agriculture.

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Cite

BibTeXRIS
BibTeX
@article{hueller2026synthetic,
  title = {{Synthetic Apple Collection (SyntAC): render-based synthetic datasets for apple detection.}},
  author = {Jhonny Hueller and Massimo Vecchio and Fabio Antonelli},
  journal = {Data in brief},
  year = {2026},
  month = aug,
  doi = {10.1016/j.dib.2026.113368},
  url = {https://europepmc.org/article/MED/42999497},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Synthetic Apple Collection (SyntAC): render-based synthetic datasets for apple detection.
AU  - Jhonny Hueller
AU  - Massimo Vecchio
AU  - Fabio Antonelli
PY  - 2026
DA  - 2026/08/01
JO  - Data in brief
DO  - 10.1016/j.dib.2026.113368
UR  - https://europepmc.org/article/MED/42999497
AB  - We present the Synthetic Apple Collection (SyntAC), a 20 GB set of synthetic images developed for apple detection in RGB images. The collection consists of 3 datasets, each containing 3840 image samples, for a total of 11,520 images, generated using Unreal Engine 5 and annotated in the YOLO format. The datasets include a variety of scenes in a virtual environment representing an apple orchard in different illumination conditions. Annotations are produced procedurally from engine data, providing perfect, pixel-to-pixel labeling and ensuring full reproducibility of the annotation process. The collection is designed to be easily reusable for the development, testing, and benchmarking of object detection algorithms and, more generally, machine learning models for precision agriculture.
SN  - 2352-3409
ER  -