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Ripe Strawberries Object Detection dataset

The dataset consists of photos of strawberries for the identification and recognition of ripe berries. The images are annotated with bounding boxes that accurately demarcate the location of the ripe strawberries within the image.

๐Ÿ’ด For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

Each image in the dataset showcases a strawberry plantation, and includes a diverse range of backgrounds, lighting conditions, and orientations. The photos are captured from various angles and distances, providing a realistic representation of strawberries.

The dataset can be utilised for enabling advancements in strawberry production, quality control, and greater precision in agricultural practices.

Dataset structure

  • images - contains of original images of strawberries
  • boxes - includes bounding box labeling for the original images
  • annotations.xml - contains coordinates of the bounding boxes and labels, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes for ripe strawberries detection. For each point, the x and y coordinates are provided. Visibility of the ripe strawberry is also provided by the attribute occluded (0, 1).

Example of XML file structure

Strawberry Detection might be made in accordance with your requirements.

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TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

keywords: strawberry harvesting, ripeness, strawberry classification, mature strawberries, unripe, raw, overripe, detection system, harvestation stages, pluck strawberries, strawberry identification, berries, plantations, agriculture, mature fruit, greenhouse strawberries, recognition accuracy, flowers, software development, image dataset, segmentation, object detection, bounding boxes

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