构建了7种观赏藤本的高分辨率图像数据集，用于精准园艺与城市生态中的实例分割研究。
Vines-DB: An RGB image dataset for multi-species ornamental vine segmentation
- 采集7种藤本植物在真实田间环境下的多月重复拍摄图像
- 包含2307张标注图像，支持多类别实例分割模型训练与评估
- 适用于自动化冠层覆盖估算、物种识别等农业智能应用
Vines-DB 数据集包含1,218张高分辨率RGB图像，涵盖7种观赏藤本植物，采集自美国犹他州格林维尔研究农场的田间环境。这些图像源于2022年移植的168株藤本，在2023至2024年生长季（7月至10月）内每月重复拍摄，使用配备4800万像素摄像头的iPhone 16 Pro于上午10点至12点间采集。藤本种植于1.2m×2.4m的支架上，距拍摄点1米处以黑白泡沫板为背景，提升对比度并减少背景噪声。样本包括Akebia quinata、Campsis radicans、Hydrangea anomala petiolaris、Lonicera x heckrottii、Campsis x tagliabuana 'Madame Galen'、Parthenocissus quinquefolia和Wisteria floribunda。所有原始图像经专业标注员在Roboflow中手工绘制多边形实例分割掩码，共生成8类（7个物种+背景）。经预处理与数据增强后，工作数据集扩展至2,307张图像，按分层抽样划分为2,019张训练集、192张验证集和96张测试集。该数据集支持精准园艺与城市生态中深度学习模型的开发与评估，可用于自动化冠层覆盖估计、物种识别与可扩展田间表型分析。每月重复成像捕捉了冠层发育的时序变化，提升了其在真实田间条件下分割基准测试的实用性。
原文摘要 · Abstract (English)
The Vines-DB dataset contains 1,218 original high-resolution RGB images of seven ornamental vine species collected under field conditions at the Utah Agricultural Experiment Station's Greenville Research Farm in Logan, Utah, USA. The dataset was generated from 168 individual vine plants that were transplanted in 2022 and photographed repeatedly across multiple months during the 2023 and 2024 growing seasons (July-October). Images were captured with an iPhone 16 Pro equipped with a 48 MP camera between 10:00 AM and 12:00 PM under daylight. Vines were grown on 1.2m x 2.4m trellises and photographed from a distance of 1m against black or white Styrofoam backdrops to improve contrast and reduce background noise. The dataset includes Akebia quinata, Campsis radicans, Hydrangea anomala petiolaris, Lonicera x heckrottii, Campsis x tagliabuana 'Madame Galen', Parthenocissus quinquefolia, and Wisteria floribunda. All original images were manually annotated in Roboflow by trained annotators to produce polygon-based instance segmentation masks for eight classes, including seven species and background. After preprocessing and data augmentation, the working dataset was expanded to 2,307 images for model development and evaluation. The augmented dataset was divided into 2,019 training images, 192 validation images, and 96 test images using stratified sampling to maintain balanced representation. Vines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology. The dataset enables applications such as automated canopy cover estimation, species identification, and scalable field phenotyping. In addition, repeated monthly imaging of the plants captures temporal variation in canopy development and plant appearance, increasing the dataset's utility for segmentation benchmarking under realistic field conditions.
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