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Ripe Berry Agricultural Detection Dataset
Food & Beverage Consumption
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About
The data product provides foundational material for artificial intelligence applications in agriculture. It focuses specifically on the challenge of reliably identifying ripe strawberries using visual data. The high-quality image collection is fully annotated with bounding boxes, accurately delineating the location of mature fruit within the photos. This resource can drive forward greater precision in agricultural practices, improve quality control processes, and enable significant advancements in automated strawberry production and harvestation stages.
Columns
The dataset structure relies on original image files paired with XML annotations. The core data elements contained within the annotations define the precise location and characteristics of detected objects:
- Coordinates: Provide the x and y values for the bounding boxes that demarcate the location of ripe strawberries.
- Labels: Identify the object type (ripe strawberry).
- Occluded Attribute: A binary attribute (0 or 1) indicating the visibility of the specific berry instance within the frame.
Distribution
This resource is structured into two main components: an
images folder containing the raw photos and a boxes section including the bounding box labeling. Annotation details are stored in the annotations.xml file, which includes coordinates and labels for each original photo. The size of the annotations.xml file is approximately 68.11 kB. The underlying Object Detection model utilises a rectangle definition for its bounding boxes.Usage
This data is ideally suited for:
- Developing object detection systems for real-time agricultural applications.
- Improving automated sorting and quality control mechanisms.
- Training machine learning models focused on strawberry classification and ripeness detection.
- Enabling sophisticated software development for automated strawberry harvesting systems.
Coverage
The data reflects scenes from strawberry plantations, including visual representations of greenhouse strawberries. The images capture realistic field conditions, showing various harvestation stages, including flowers, unripe, and mature fruit. The data acquisition date noted for one job was 2023-09-01.
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Who Can Use It
The dataset is essential for anyone engaged in computer vision for food technology or precision farming. Intended users include:
- Agricultural Technology Companies: Utilising the data to build or refine automated harvesting robots and field monitoring tools.
- Computer Vision Developers: Focusing on high-accuracy object recognition within complex, natural backgrounds.
- Academic Researchers: Studying fruit classification, image segmentation, and ripeness detection accuracy.
Dataset Name Suggestions
- Ripe Berry Agricultural Detection Dataset
- Precision Strawberry Harvesting Image Data
- Mature Fruit Recognition Object Detection
- Strawberry Ripeness Detection System Training Data
Attributes
Original Data Source: Ripe Berry Agricultural Detection Dataset
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