K1702 - Kura Clover (Trifolium ambiguum) USDA Accession Image Dataset

Images This dataset consists of 1135 images of Kura clover (Trifolium ambiguum) USDA accessions grown at The Land Institute in Salina, Kansas over the 2017 growing season. Each image contains a single Kura clover plant framed by a 1/2" PVC sampling quadrat with internal dimensions of 16"x16" (internal area of 0.165 m2). Kura clover plots were hand weeded to remove all other vegetation except Kura clover. Some images may contain dead clover accessions that are either brown and dried up, or missing entirely. The images were acquired with a Canon EOS Rebel T6 DSLR camera under the following settings: ISO: 200 Exposure: Auto Focal Length: Variable (33-40mm) Format: JPEG Size: 5184x3456 Metering Mode: Multi-segment Images were acquired on two different dates: 2017-06-08 and 2017-07-03 and were named using the following convention "<IMG_ID>_<yyyymmdd>.jpg". All image can be found in processed/images folder. No image preprocessing was performed. Annotations The annotations consist of segmentation masks and bounding boxes. Each segmentation mask is saved as a png image and named using the convention "IMG_ID>_<yyyymmdd>.png". The segmentation class labels ('segmentation_class_map.json') are as follows: 0: 'soil' background class containing all soil and non-target materials 1: 'quadrat' 2: 'clover' We drew bounding boxes for the quadrat, each quadrat corner, and the entire clover plant. The class labels ('obj_det_class_map.json') are as follows: 1: 'clover' 2: 'quadrat' 3: 'quadrat_corner' Bounding boxes are in (xmin, ymin, xmax, ymax) format and can be found in 'bboxes.csv'. All images are annotated using Labelbox software. Masks were generated by point prompts using Meta's Segment Anything model (SAM). The point prompts used to generate the masks can be found in 'SAM_points.csv' Additionally, some kura clover plants died or are not present in the plots where they were planted. We included the file 'plant_status.csv' to indicate which images include a living plant or a dead one. Train/Val/Test Split All 1035 images were randomly split with an 80/20 split on 1000 of the images (n=880, n=220) with the final 35 images reserved for the test holdout set. The file 'data_split.csv' holds the split class for each image. This dataset is released under a Creative Commons Attribution 4.0 International license which allows redistribution and re-use of the data herein as long as all authors are appropriately credited.### 图像集 本数据集包含1135张库拉三叶草（*Trifolium ambiguum*）美国农业部（United States Department of Agriculture, USDA）种质资源的图像，这些植株种植于堪萨斯州萨利纳市土地研究所（The Land Institute）2017年生长季的试验田中。每张图像均包含单株库拉三叶草，由内部尺寸为16英寸×16英寸（对应面积0.165平方米）的1/2英寸聚氯乙烯（PVC）采样样方框框定。库拉三叶草试验地块均经人工除草，仅保留库拉三叶草植株。部分图像中可能包含枯死的三叶草种质，表现为棕褐色干枯状态，或植株完全缺失。本数据集采用佳能EOS Rebel T6单反相机拍摄，拍摄参数如下： ISO：200 曝光模式：自动 焦距：可变（33-40mm） 图像格式：JPEG 分辨率：5184×3456 测光模式：多分区测光 图像采集日期为2017年6月8日与2017年7月3日，命名规则为`<IMG_ID>_<yyyymmdd>.jpg`。所有图像均存储于`processed/images`文件夹下，未进行任何图像预处理。 ### 标注信息 本数据集的标注包含分割掩码与边界框两类。每个分割掩码以PNG格式存储，命名规则为`<IMG_ID>_<yyyymmdd>.png`。分割类别标签（`segmentation_class_map.json`）如下： 0：土壤背景类，包含所有土壤及非目标物体 1：采样样方框 2：三叶草 我们为采样样方框、采样样方框的四个角点以及整株三叶草绘制了边界框。目标检测类别标签（`obj_det_class_map.json`）如下： 1：三叶草 2：采样样方框 3：采样样方框角点 边界框采用`(xmin, ymin, xmax, ymax)`格式，存储于`bboxes.csv`文件中。所有图像均通过Labelbox软件完成标注，掩码由Meta的Segment Anything模型（SAM）通过点提示生成，用于生成掩码的点提示存储于`SAM_points.csv`文件中。 此外，部分库拉三叶草植株死亡或未在预设种植地块中存活，我们提供了`plant_status.csv`文件，用于标注每张图像对应的植株为存活状态还是枯死状态。 ### 训练/验证/测试集划分 本数据集的1035张图像按如下方式随机划分：其中1000张以80:20的比例划分为训练集与验证集（训练集n=880，验证集n=220），剩余35张作为测试保留集。数据集划分信息存储于`data_split.csv`文件中。 本数据集采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International）发布，允许对本数据集进行再分发与再利用，前提是对所有原作者予以适当署名。