Synthetic RGB-D Data for Plant Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 10000 images with 326754 labeled objects belonging to 4 different classes including leaf, petiole, stem, and other: fruit.

Images in the Synthetic Plants dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 2 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2018.

Here is the visualized example grid with animated annotations:

Synthetic RGB-D Data for Plant Segmentation is under CC0 1.0 license.

Source

If you make use of the Synthetic Plants data, please cite the following reference:

@dataset{Synthetic Plants,
	author={El Baha Farouk},
	title={Synthetic RGB-D Data for Plant Segmentation},
	year={2018},
	url={https://www.kaggle.com/datasets/harlequeen/synthetic-rgbd-images-of-plants}
}

Source

Dataset Synthetic Plants can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Synthetic Plants', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.