2SeasonWeedDet8: a two-season, 8-class dataset for cross-season weed detection generalization evaluation
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The 2SeasonWeedDet8 dataset comprises two eight-class sub-datasets acquired in two consecutive seasons of 2021 and 2022. It was specifically curated for assessing the cross-season generalization assessment of weed detection models. Images both years were captured for naturally germinated weeds using smartphone or digital color cameras in cotton fields across Mississippi. Images were manually labeled by qualified personnel who draw bounding boxes for individual weed plants using the VGG Image Annotator (version 2.10). Initial annotations were examined by PI or trained personnel for weed identification for quality control before inclusions in the final dataset. Weed classes of the dataset include: Waterhemp, Carpetweed, Morninglory, Goosegrass, Spotted Spurge, Palmer Amaranth, Purslane, and Ragweed. Each weed image (in .jpg fomat) has one corresponding annotation file of the same file name, in both JSON and XML formats, placed in the same folder. For the JSON file, the annotated bounding box is defined in COCO format, i.e., [x_min, y_min, width, height]. For the XML file, the annotated bounding box is represented in Pascal VOC format, i.e., [x_min, y_min, x_max, y_max]. For the two sub-datasets (corresponding to the compressed files, "Year2021" and "Year2022") Weed Data of Year 2021: derived from the CottonWeedDet12 dataset, the sub-dataset contains 4734 images with 7664 bounding boxes. It is broken down into two compressed files "Year2021_Part1" (with 2290 images) and "Year2021_Part2" (with 2444 images) for the convenience of data uploading and downloading. After downloading and unzipping the two files, you may merge them together for the complete data of Year 2021. Weed Data of Year 2022: this sub-dataset consists of 1930 images with 3184 bounding boxes The combined two-season dataset has 6664 images with 10848 bounding boxes. More detailed documentation of the dataset curation and model benchmarking for weed detection are described in the accompanying journal paper: Deng, B., Lu, Y., & Xu, J. (2024). Weed Database Development: An Updated Survey of Public Weed Datasets and Cross-Season Weed Detection Adaptation. Ecological Informatics, 102546. https://doi.org/10.1016/j.ecoinf.2024.102546. If you use the dataset in published research, please consider citing the dataset or associated journal article above. Hopefully, you find this dataset useful.
2SeasonWeedDet8数据集包含两个分别采集于2021年与2022年连续生长季的八分类子数据集，其专为评估杂草检测模型的跨季泛化能力而构建。 两年的图像均通过智能手机或数码彩色相机，在密西西比州的棉田内对自然萌发的杂草进行拍摄。所有图像均由合格人员使用VGG图像标注工具（VGG Image Annotator，版本2.10）为单株杂草绘制边界框完成手动标注。初始标注完成后，由项目负责人（Principal Investigator，简称PI）或经过培训的专业人员针对杂草种类识别进行审核，以完成质量控制，之后才可纳入最终数据集。 本数据集涵盖8类杂草：水苘麻（Waterhemp）、星苞草（Carpetweed）、牵牛花（Morninglory）、马唐草（Goosegrass）、斑地锦（Spotted Spurge）、帕尔默苋（Palmer Amaranth）、马齿苋（Purslane）以及豚草（Ragweed）。 每幅杂草图像均采用.jpg格式存储，对应拥有同名的标注文件，包含JSON与XML两种格式，且与图像置于同一文件夹下。其中JSON格式的标注边界框采用COCO格式（Common Objects in Context）定义，即[x_min, y_min, width, height]；XML格式的标注边界框则采用PASCAL VOC格式表示，即[x_min, y_min, x_max, y_max]。 两个子数据集分别对应压缩文件"Year2021"与"Year2022"： 1. 2021年杂草数据集：该子数据集衍生自CottonWeedDet12数据集，包含4734张图像与7664个边界框。为便于数据上传与下载，其被拆分为两个压缩文件"Year2021_Part1"（含2290张图像）与"Year2021_Part2"（含2444张图像）。用户下载并解压这两个文件后，可将其合并以获得完整的2021年数据集。 2. 2022年杂草数据集：该子数据集包含1930张图像与3184个边界框。 两个生长季的数据集合并后，总计包含6664张图像与10848个边界框。 关于本数据集构建与杂草检测模型基准测试的详细说明，请参阅配套期刊论文：Deng, B., Lu, Y., & Xu, J. (2024). 杂草数据库开发：公开杂草数据集的最新综述与跨季杂草检测适配方法. 《生态信息学（Ecological Informatics）》, 102546. https://doi.org/10.1016/j.ecoinf.2024.102546. 若您在已发表的研究中使用本数据集，请考虑引用本数据集或上述期刊论文。希望本数据集能对您的研究有所帮助。
