Global Wheat Head Dataset 2021

This is the full Global Wheat Head Dataset 2021. Labels are included in csv. Tutorials available here: https://www.aicrowd.com/challenges/global-wheat-challenge-2021 🕵️ Introduction Wheat is the basis of the diet of a large part of humanity. Therefore, this cereal is widely studied by scientists to ensure food security. A tedious, yet important part of this research is the measurement of different characteristics of the plants, also known as Plant Phenotyping. Monitoring plant architectural characteristics allow the breeders to grow better varieties and the farmers to make better decisions, but this critical step is still done manually. The emergence of UAV, camera and smartphone makes in-field RGB images more available and could be a solution to manual measurement. For instance, the counting of the wheat head can be done with Deep Learning. However, this task can be visually challenging. There is often an overlap of dense wheat plants, and the wind can blur the photographs, making identify single heads difficult. Additionally, appearances vary due to maturity, colour, genotype, and head orientation. Finally, because wheat is grown worldwide, different varieties, planting densities, patterns, and field conditions must be considered. To end manual counting, a robust algorithm must be created to address all these issues. 💾 Dataset The dataset is composed of more than 6000 images of 1024x1024 pixels containing 300k+ unique wheat heads, with the corresponding bounding boxes. The images come from 11 countries and covers 44 unique measurement sessions. A measurement session is a set of images acquired at the same location, during a coherent timestamp (usually a few hours), with a specific sensor. In comparison to the 2020 competition on Kaggle, it represents 4 new countries, 22 new measurements sessions, 1200 new images and 120k new wheat heads. This amount of new situations will help to reinforce the quality of the test dataset. The 2020 dataset was labelled by researchers and students from 9 institutions across 7 countries. The additional data have been labelled by Human in the Loop, an ethical AI labelling company. We hope these changes will help in finding the most robust algorithms possible! The task is to localize the wheat head contained in each image. The goal is to obtain a model which is robust to variation in shape, illumination, sensor and locations. A set of boxes coordinates is provided for each image. The training dataset will be the images acquired in Europe and Canada, which cover approximately 4000 images and the test dataset will be composed of the images from North America (except Canada), Asia, Oceania and Africa and covers approximately 2000 images. It represents 7 new measurements sessions available for training but 17 new measurements sessions for the test! 📁 Files Following files are available in the resources section: images: the folder contains all images competition_train.csv , competition_val.csv, competition_test.csv : contains the splits used for the 2021 Global Wheat Challenge Val contains the "public test", which is the test set of Global Wheat Head 2020 Test contains the "private test". Metadata.csv : contains additional metadatas for each domain 💻 Labels All boxes are contained in a csv with three columns image_name, BoxesString and domain image_name is the name of the image, without the suffix. All images have a .png extension BoxesString is a string containing all predicted boxes with the format [x_min,y_min, x_max,y_max]. To concatenate a list of boxes into a PredString, please concatenate all list of coordinates with one space (" ") and all boxes with one semi-column ";". If there is no box, BoxesString is equal to "no_box". domain give the domain for each image If you use the dataset for your research, please do not forget to quote: @article{david2020global,
title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},
author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others},
journal={Plant Phenomics},
volume={2020},
year={2020},
publisher={Science Partner Journal}
}
@misc{david2021global, title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods}, author={Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto Espinosa and Shahameh Shafiee and Izzat S. A. Tahir and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kichgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and Sébastien Dandrifosse and Alexis Carlier and Benoit Mercatoris and Ken Kuroki and Haozhou Wang and Masanori Ishii and Minhajul A. Badhon and Curtis Pozniak and David Shaner LeBauer and Morten Lilimo and Jesse Poland and Scott Chapman and Benoit de Solan and Frédéric Baret and Ian Stavness and Wei Guo}, year={2021}, eprint={2105.07660}, archivePrefix={arXiv}, primaryClass={cs.CV} }

本数据集为完整的2021年全球小麦穗数据集（Global Wheat Head Dataset 2021），标签以CSV格式提供。教程可访问：https://www.aicrowd.com/challenges/global-wheat-challenge-2021

🕵️ 研究背景
小麦是全球绝大多数人口的饮食基础，因此科学家们对该谷物开展了广泛研究以保障粮食安全。其中一项繁琐却至关重要的研究环节，便是对植物各项特征进行测量，即植物表型组学（Plant Phenotyping）。通过监测植物的结构特征，育种者可培育出更优良的品种，农户也能做出更科学的决策，但这一关键环节目前仍依赖人工完成。
无人机、相机与智能手机的普及使得田间RGB图像获取变得更加便捷，有望成为替代人工测量的解决方案。例如，可借助深度学习实现小麦穗计数，但该任务视觉难度较高：密集种植的小麦植株常出现重叠，风力还可能导致照片模糊，令单穗识别变得困难。此外，小麦穗的外观会因成熟度、颜色、基因型以及朝向的不同而存在差异。加之小麦在全球范围内广泛种植，不同品种、种植密度、种植模式与田间环境均需被纳入考量。为终结人工计数的现状，亟需开发出能够应对上述所有挑战的鲁棒算法。

💾 数据集概况
本数据集包含超过6000张分辨率为1024×1024像素的图像，共标注有30万余个独特小麦穗，附带对应的边界框。这些图像源自11个国家，涵盖44次独立测量会话。一次测量会话指的是在同一地点、同一连贯时间段（通常为数小时）内，使用特定传感器采集的一组图像。
与2020年Kaggle竞赛所用数据集相比，本次新增了4个国家、22次测量会话、1200张图像以及12万个小麦穗标注。新增的多样化样本将有助于提升测试集的质量。2020年数据集由来自7个国家9个机构的研究人员与学生完成标注，新增数据则由伦理AI标注公司Human in the Loop完成。我们期望此次更新能够助力开发出鲁棒性更强的算法！
本任务的目标是定位每张图像中的小麦穗，期望得到能够适应形状、光照、传感器与拍摄场景变化的模型。每张图像均附带一组边界框坐标。训练集包含源自欧洲与加拿大的图像，共计约4000张；测试集则由北美（加拿大除外）、亚洲、大洋洲与非洲的图像组成，共计约2000张。这意味着训练集新增了7次测量会话，而测试集则新增了17次测量会话！

📁 数据集文件
资源区包含以下文件：
images：存储所有图像的文件夹
competition_train.csv、competition_val.csv、competition_test.csv：包含2021年全球小麦挑战赛所用的数据集划分信息。其中val对应2020年全球小麦穗数据集的公开测试集，test对应私有测试集。
Metadata.csv：包含各图像域的额外元数据。

💻 标签说明
所有标签均存储于CSV文件中，包含三列：image_name、BoxesString与domain。
- image_name：图像文件名（不含后缀），所有图像均为.png格式
- BoxesString：包含所有边界框的字符串，格式为[x_min,y_min, x_max,y_max]。若要将多个边界框列表拼接为BoxesString，需使用单个空格分隔坐标，使用单个分号分隔不同边界框。若无边界框，则BoxesString取值为"no_box"。
- domain：标识每张图像所属的域。

若您在研究中使用本数据集，请引用以下文献：
@article{david2020global,
title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},
author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others},
journal={Plant Phenomics},
volume={2020},
year={2020},
publisher={Science Partner Journal}
}
@misc{david2021global, title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods}, author={Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto Espinosa and Shahameh Shafiee and Izzat S. A. Tahir and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kichgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and Sébastien Dandrifosse and Alexis Carlier and Benoit Mercatoris and Ken Kuroki and Haozhou Wang and Masanori Ishii and Minhajul A. Badhon and Curtis Pozniak and David Shaner LeBauer and Morten Lilimo and Jesse Poland and Scott Chapman and Benoit de Solan and Frédéric Baret and Ian Stavness and Wei Guo}, year={2021}, eprint={2105.07660}, archivePrefix={arXiv}, primaryClass={cs.CV} }

搜集汇总

背景与挑战

背景概述

Global Wheat Head Dataset 2021是一个用于小麦头检测的大规模计算机视觉数据集，包含超过6000张高分辨率图像和300k+个标注的小麦头，覆盖11个国家和44个测量会话，旨在通过增加地理和条件多样性来提升模型的鲁棒性。数据集主要用于训练和测试小麦头定位算法，支持植物表型研究，以自动化小麦计数并替代传统手动测量方法。