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

AerialYield-B2D：包含五个成熟阶段掩码与果实计数的温室蓝莓数据集

Abstract: 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. 摘要： 蓝莓的成熟度通常通过浆果颜色、果簇构成以及植株内成熟阶段的分布来判断。然而，目前公开的、带有密集成熟阶段掩码的温室图像资源仍然十分有限。

We present AerialYield-B2D, where B2D denotes BlueBerry Dataset, a curated 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. 我们推出了 AerialYield-B2D（B2D 代表蓝莓数据集），这是一个精心策划的真实图像资源库，包含 514 张 RGB 图像和 30,195 个已标注的蓝莓实例，涵盖了五个成熟阶段：绿色未成熟、浅粉色、粉转紫色、完全成熟和过熟。

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. 该数据集提供了特定类别的二值掩码、整体浆果掩码、语义标签图、图像级计数表、SHA-256 哈希值、源元数据、推荐的训练/验证/测试集划分以及技术验证。

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. AerialYield 是该项目的统称；本次发布的数据集不提供收获重量、果实质量或单位面积产量测量值，因此计数标签应被理解为图像级的浆果数量，而非产量估算。

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. 这些图像包括 424 张智能手机拍摄的温室照片、67 帧视频提取图像以及 23 个 DJI Fly 视频帧样本，为受控环境下的蓝莓生产提供了可复现的成熟度分割、浆果计数和类别不平衡分析数据集。