Cranberry Fruit Images and Segmentation Annotations from a Postharvest Imaging Platform

210 raw images of cranberry fruit with YOLOv8 format segmentation annotations, collected using a Nikon D7500 DSLR camera in a custom-built LED lightbox. The dataset was created by the Department of Agriculture to train an instance segmentation model for downstream analysis of berry color, size, shape, and uniformity. It was last updated on March 13, 2026.

Use Cases

• Training instance segmentation models based on the provided YOLOv8 annotations.
• Developing computer vision pipelines for measuring fruit color, size, and shape uniformity as described.
• Benchmarking agricultural image analysis models using a dataset of 210 annotated images.
• Researching postharvest fruit quality assessment based on visual characteristics.

Strengths

• Contains 210 annotated images, providing a defined scale for model training.
• Annotations are in a standard YOLOv8 format, which is widely supported by machine learning frameworks.
• Imaging conditions are documented, including camera model and manual settings for shutter speed, focal length, f-stop, and ISO.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific model architectures.

Provenance

Source

Department of Agriculture

Collection Method

Images collected using a Nikon D7500 DSLR camera in a custom-built LED lightbox and sample tray.

Time Range

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Freshness

Last updated 2026-03-13 22:44:31.900016; freshness should be verified.

Geography

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