FlexiGroBots Ground-level Blueberry Orchard Dataset v1 - RGB Bush Detection Dataset
Authors/Creators
- 1. BioSense Institute
- 2. Aurora Innovation
Description
Ground-level Blueberry Orchard Dataset v1 consists of 2000 RGB images of blueberry orchard scenes captured in the village of Babe, Serbia on three occasions in March, May, and August of 2022. Images are captured using the RGB module of Luxonis OAK-D device, with the resolution of 1920×1080 pixels and stored in the lossless PNG format.
The dataset is created for the purpose of training deep learning models for blueberry bush detection, for the task of autonomous UGV guidance. It contains sequences of images captured from the UGV moving and rotating in blueberry orchard rows. Images are captured from a height of approximately 0.5 meters, with the camera angled towards the base of a blueberry plant and the surrounding bank on which it grows. Dataset is captured in real-life outdoor conditions and contains multiple sources of variability (bush shape and size, lighting conditions, shadows, saturation etc.) and artifacts (occlusions by weeds, branches, presence of irregular objects etc.).
There are two classes of annotated objects of interest:
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Bush, corresponding to the base of the blueberry bush.
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Pole, corresponding to hail netting poles and similar obstructing objects such as lamp posts or wooden legs of bumblebee hives (distinguishing poles is important to prevent equipment damage in operations such as soil sampling and pruning).
Objects of interest are annotated with bounding boxes. Labels are saved in two formats:
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LabelMe JSON format (x1, y1, x2, y2; in pixels)
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Yolo TXT format (x_center, y_center, width, height; as a ratio of total image size, with numerical labels 0 and 1 corresponding to Bush and Pole)
There are 61 images with no annotated objects, and there are no corresponding label files for these images.
The dataset is split into train, validation and test sets with 75%, 10%, and 15% split (1490, 200, and 310 images, respectively). As the data contains sequences of images, the split is made based on sequences rather than individual images to prevent data leakage.
Detailed description and statistics are available in:
V. Filipović, D. Stefanović, N. Pajević, Ž. Grbović, N. Đurić and M. Panić, "Bush Detection for Vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, Canada, 2023. (Accepted)