# AerialYield-B2D: A Greenhouse Blueberry Dataset with Five-Stage Ripeness Masks and Fruit Counts

arXiv: https://arxiv.org/abs/2608.16973 · HTML: https://mlsanity.com/paper/2608.16973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping](https://mlsanity.com/paper/2507.11279/) (2025)
- [An annotated grain kernel image database for visual quality inspection](https://mlsanity.com/paper/2401.08599/) (2023)
- [A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management](https://mlsanity.com/paper/2509.20580/) (2025)
- [A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation](https://mlsanity.com/paper/2503.01605/) (2025)

## Abstract (arXiv)

Blueberry ripeness is judged by berry colour, cluster composition, and the distribution of maturity stages within a plant, however, public green house image resources with dense ripeness-stage masks remain limited. We present AerialYield-B2D, where B2D denotes BlueBerry Dataset, acurated real-image resource containing 514 RGB images and 30,195 annotated blueberry instances across five ripeness stages: green immature, pale pink, pink-turns-purple, fully ripe and over-ripe. The release provides class-specific binary masks, overall berry masks, semantic label maps, image-level count tables, SHA-256 hashes, source metadata, recommended train/validation/test splits and technical validations. AerialYield is the broader project name; this release does not provide harvest weight, fruit mass or per-area yield measurements, and the count labels should therefore be interpreted as image-level berry counts rather than yield estimates. The images include 424 smartphone greenhouse images, 67 video-derived frames, and 23 DJI Fly video-frame samples, providing a reproducible dataset for ripeness segmentation, berry counting, and class-imbalance analysis in controlled-environment blueberry production.