GobhiSet: Dataset of raw, manually and automatically annotated RGB images across phenology of Brassica oleracea var. Botrytis

This dataset encompasses a compilation of unprocessed aerial RGB images and orthomosaics. These images, captured via a DJI Phantom 4, span several dates and depict Brassica oleracea crops. The images are uniformly distributed across crop spaces and have undergone both manual and automatic annotation. This data pool is engineered to facilitate the detection, segmentation, and growth modelling of crops, utilizing pixel information annotated both manually and automatically. The publicly accessible repository houses 244 raw RGB images, acquired over six distinct dates in October and November of 2020. The experimental farm is located in Portici, Italy. Each raw image bears a dimension of 5472×3648 pixels. The initial three sets of images, captured on October 8, 2020, October 21, 2020, and October 29, 2020, were manually annotated using bounding boxes via the Visual Geometry Group Image Annotator (VIA). These annotations were exported in the Common Objects in Context (COCO) segmentation format. The manual labelling data of the imagery dated October 8, October 21, and October 29, including region and shape attributes, is detailed in JavaScript Object Notation (JSON). These three dates served as training data for the annotator to improve the automated labelling across all dates: 8 October, 21 October, 29 October, 11 November, 18 November, and 25 November. The benchmark annotation was noted to be of 21 October, 2020, in terms of quantitative assessment criteria. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling within them. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. For the generation of automated annotations, the manual annotations were trained over a framework of Grounding DINO + Segment Anything Model (SAM), and the labels were archived in Pascal Visual Object Classes (PASCAL VOC) format. The segmentation masks, derived from automated annotations, are furnished in the form of Portable Network Graphics (PNG) images, catering to three distinct scenarios: aerial images, individual crop rows, and orthomosaics. These automated annotations facilitate the monitoring of growth across the crop phenology, employing evaluation based on binary masks of individually identified crop rows, captured across various dates. The codes utilized for these processes are accessible to ensure transparency and reproducibility. The dataset not only furnishes annotation information but can also assist in the refinement of various machine learning models.

本数据集涵盖了一组未经处理的航空RGB影像和正射影像的汇编。这些影像由DJI Phantom 4相机捕捉，跨越数日，描绘了Brassica oleracea作物的生长状况。影像在作物区域内均匀分布，并经历了人工与自动标注的双重过程。该数据集旨在促进作物检测、分割和生长建模，利用人工和自动标注的像素信息。公开可访问的存储库中包含了244张原始RGB影像，这些影像于2020年10月和11月的六天内获取。实验农场位于意大利的Portici。每张原始影像的分辨率为5472×3648像素。前三组影像，分别于2020年10月8日、10月21日和10月29日捕捉，通过视觉几何组图像标注器（VIA）进行人工标注，标注内容以边界框的形式呈现。这些标注以Common Objects in Context（COCO）分割格式导出。10月8日、10月21日和10月29日的标注数据，包括区域和形状属性，以JavaScript Object Notation（JSON）格式详细记录。这三个日期的数据作为训练数据，以提升所有日期的自动标注效果：8 October, 21 October, 29 October, 11 November, 18 November, 和 25 November。在定量评估标准下，以2020年10月21日的标注作为基准。作物标注中识别出了七个类别，分别命名为行1至行7。在人工标注的CSV版本中，还详细描述了单个作物的ID和单个作物样本的重复性。为生成自动化标注，人工标注在Grounding DINO + Segment Anything Model（SAM）框架上进行训练，标签以Pascal Visual Object Classes（PASCAL VOC）格式存档。由自动化标注生成的分割掩码以Portable Network Graphics（PNG）图像的形式提供，涵盖了三种不同场景：航空影像、单个作物行和正射影像。这些自动化标注有助于监控作物生长的物候变化，通过二值掩码评估在各个日期捕捉到的单个作物行的识别情况。用于这些过程的代码可供查阅，以确保透明度和可重复性。该数据集不仅提供了标注信息，还有助于优化各种机器学习模型。