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A multi-stage, pixel-level annotated apple dataset for precision agriculture research.
Dandan Wang1,2, Bo Wang3
1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
Data in Brief
|June 4, 2026
Summary
A new dataset of 1406 apple images captures three growth stages for computer vision models. This resource aids in developing AI for precision agriculture, improving fruit detection and maturity grading.
Area of Science:
- Computer Vision
- Agricultural Science
- Machine Learning
Background:
- Developing accurate AI models for agriculture requires diverse, annotated datasets.
- Existing datasets often lack sufficient data for transitional growth stages, hindering model generalization.
Purpose of the Study:
- To introduce a comprehensive dataset of apple images covering immature, semi-mature, and mature stages.
- To provide pixel-level instance segmentation masks for training deep learning models.
- To support research in fruit detection, segmentation, and growth-stage classification.
Main Methods:
- Collected 1406 RGB images of apples (Malus domestica) across three growth stages.
- Annotated images with pixel-level instance segmentation masks in JSON format using VGG Image Annotator (VIA).
- Included real-world variability such as lighting conditions, occlusions, and clustered fruit.
Main Results:
- The dataset contains 2574 annotated apple instances.
- Images represent immature (green), semi-mature (transition), and mature (red) apples.
- The dataset variability enhances model generalizability for precision agriculture tasks.
Conclusions:
- This dataset addresses the scarcity of annotated data for transitional apple maturity phases.
- It facilitates the development of robust AI solutions for orchard management and agricultural robotics.
- The resource accelerates research in data-driven horticultural practices and transfer learning.
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