Image · 3D / Point cloud / Manufacturing & Industrial
Synthetic Strawberry Dataset for Computer Vision, Object Detection and Segmentation
Advertised by providerLicensing rights not independently verifiedPrice on request
Overview
Synthetic Strawberry Dataset for Computer Vision, Object Detection and Segmentation is an image and 3d / point cloud dataset. Typical applications include object detection, segmentation and robotics & embodied AI. Relevant sectors: Manufacturing & Industrial. The listing mentions bounding boxes and polygons / segmentation. Pricing is not published and is provided on request.
Primary applications
What the listing offers
- Listing mentions bounding boxes, polygons / segmentation.
The Synthetic Strawberry Dataset is a high-quality computer vision training dataset containing photorealistic strawberry images generated through physics-based simulation, procedural modelling, and agricultural domain expertise. The fully parameterized generation pipeline enables controlled variation of fruit characteristics, scene composition, backgrounds, lighting conditions, camera settings, and environmental factors while maintaining complete ground-truth knowledge throughout the data generation process. Designed for computer vision, robotics, and precision agriculture applications, the dataset supports tasks such as object detection, segmentation, ripeness estimation, quality assessment, yield analysis, automated harvesting, and AI benchmarking. Each sample can include rich metadata and machine-learning-ready annotations, such as bounding boxes, semantic segmentation masks, instance masks, object attributes, geometric measurements, defect labels, depth information, and scene parameters. Because the dataset is generated from a configurable simulation environment, annotation capabilities extend far beyond a predefined annotation set. Custom labels, metadata fields, task-specific ground truth, and additional annotation formats can be generated on request to meet project-specific requirements, model architectures, or validation workflows. The generation framework also supports multiple sensor modalities. In addition to RGB imagery, datasets can be delivered with outputs such
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