Vines-DB：用于多物种观赏藤蔓分割的RGB图像数据集
Vines-DB: An RGB image dataset for multi-species ornamental vine segmentation
AI总结：
为支持精准园艺和城市生态中的多类实例分割，构建了包含7种观赏藤蔓的RGB图像数据集Vines-DB，通过手动标注和增强得到2307张图像，并划分训练/验证/测试集。
摘要：
Vines-DB数据集包含在美国犹他州洛根市犹他农业实验站格林维尔研究农场田间条件下采集的7种观赏藤蔓的1,218张原始高分辨率RGB图像。该数据集来自168株于2022年移植的藤本植物，在2023和2024生长季（7月至10月）的多个月份重复拍摄。图像使用配备48 MP摄像头的iPhone 16 Pro在上午10:00至下午12:00之间于日光下拍摄。藤蔓生长在1.2m x 2.4m的格架上，从1m距离处拍摄，背景为黑色或白色泡沫板，以增强对比度并减少背景噪声。数据集包括木通、凌霄花、藤绣球、金银花、凌霄'马德琳·加伦'、五叶地锦和多花紫藤。所有原始图像由训练有素的标注员在Roboflow中手动标注，生成基于多边形的实例分割掩码，共8个类别（7个物种和背景）。经过预处理和数据增强后，工作数据集扩展至2,307张图像，用于模型开发和评估。增强后的数据集通过分层抽样划分为2,019张训练图像、192张验证图像和96张测试图像，以保持平衡的代表性。Vines-DB支持精准园艺和城市生态中多类实例分割深度学习模型的开发和评估。该数据集可实现自动冠层覆盖度估计、物种识别和可扩展的田间表型分析等应用。此外，每月重复成像捕获了冠层发育和植物外观的时间变化，增加了数据集在真实田间条件下进行分割基准测试的实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
