Vision Data for Robotic Harvesting & Crop Perception
Orchard and field imagery with maturity, occlusion and grasp-point labels for robotic harvesting and crop perception. Explore harvesting-data options with Shaip.
Dataset Specifications
| Target Buyer Teams | Agri-Robotics Lead; Computer Vision Director; Harvesting Automation Team; Product Engineering |
| Representative Tasks | Fruit detection, maturity classification, occlusion, stem/branch understanding and grasp-point estimation |
| Capture / Modalities | RGB, video, optional depth, multispectral and LiDAR |
| Annotations / Metadata | Tight boxes, masks, maturity/condition attributes, occlusion, keypoints and grasp candidates |
| Delivery Formats | JPEG/MP4 + COCO JSON + optional depth and point cloud |
| Scale (Illustrative)* | 250,000–2M images/frames |
| Participants / Operators | Not participant-dependent |
| Tasks / Scenarios | 20–100 crop/condition variants |
| Objects / Sites / Views | 25–150 farms/plots; 3–10 label layers |
| Recommended Engagement Scope | 25,000–100,000 images across 3–10 locations and 5–20 conditions |
Shaip has delivered tight apple annotations with three-layer condition attributes for robotic harvesting and yield-estimation use cases.
View the related case study →Where This Dataset Is Used
This agricultural robotics dataset is built for teams training fruit detection, maturity classification, occlusion reasoning and grasp-point estimation models for orchard and field robots. Agri-tech companies, harvesting automation teams and computer vision research labs use this robotic harvesting dataset as a fruit detection dataset and orchard vision dataset for crop perception, yield estimation and vision-guided picking. If you’re sourcing agriculture robot training data or crop perception data to move a harvesting robot from proof of concept to field deployment, this dataset provides the labeled visual foundation.
Request a Sample of the Agricultural Robotic Harvesting Vision Dataset
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