Tobacco Dataset for crop/weed classification

We have acquired a new tobacco-weed dataset using a Mavic Mini drone. Eight fields of tobacco crop are captured in Mardan, Khyber Pakhtunkhwa, Pakistan. At different growth stages these eight fields are captured at crop age of 15 to 40 days approximately. Data is captured at 1920×1080-pixel resolution. Dataset is captured at an average altitude of 4 meters with ground sampling distance of 0.1 cm/pixel. Find Details in attached Research papers Citation Request: if you use these datasets in your research or projects by any means, please cite following publications. 1) Patch-wise weeds coarse segmentation mask from aerial imagery of sesame crop (Published in Computers and Electronics in Agriculture 2022, HEC Recognized W category, Impact factor 6.757, Q1) 2) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery (Published in Smart Agricultural Technology (A companion journal of Computers and Electronics in Agriculture)) 3) A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop (Published in IEEE Access, Impact factor 3.1, Q1) Acknowledgement Request This work is funded by the Higher Education Commission of Pakistan and the National center for Robotics and Automation (DF-1009–31). Please Acknowledge. Steps to Access Mendeley datasets 1. Click on the link 2. The link with ask you to sign in or register with institutional email. 3. Use your institutional/organization email to register and then sign in. 4. Once sign in, dataset will be visible in compressed folders 5. Download and unzip/umcompress folder 6. Use dataset in your research as you see fit (folders contains original images, and their labeled groundtruths, along with binary vegetation masks. In groundtruths background have label value of 0, crop have label 1 and weeds have label of 2. maskref subfolders shows labelled data for visualization) Find More datasets and published articles in Related Links本研究采用大疆御迷你（Mavic Mini）无人机采集了一套全新的烟草-杂草数据集。数据集采集于巴基斯坦开伯尔-普赫图赫瓦省马尔丹地区的8块烟草田。该8块烟田的图像采集覆盖了烟草约15至40天的不同生长周期，图像分辨率为1920×1080像素，平均飞行高度为4米，地面采样距离（ground sampling distance）为0.1厘米/像素。 详细信息请参阅附件研究论文。 引用声明：若您在任何研究或项目中使用本数据集，请引用以下发表文献： 1) 《基于芝麻航拍图像的斑块级杂草粗分割掩码》（发表于《农业与计算机电子学（Computers and Electronics in Agriculture）》2022年，获巴基斯坦高等教育委员会（HEC）W类认证，影响因子6.757，属于Q1分区期刊） 2) 《基于航拍图像中烟草与杂草像素的两阶段语义分割实现杂草自动化检测》（发表于《智能农业技术（Smart Agricultural Technology）》，系《农业与计算机电子学（Computers and Electronics in Agriculture）》的姊妹期刊） 3) 《基于斑块图像分类的甜菜田杂草检测方法》（发表于《IEEE Access》，影响因子3.1，Q1分区期刊） 致谢声明：本研究受巴基斯坦高等教育委员会与国家机器人与自动化中心（项目编号DF-1009–31）资助，请在使用时予以致谢。 Mendeley数据集获取步骤： 1. 点击对应链接 2. 链接将引导您使用机构邮箱登录或注册 3. 使用您的机构/组织邮箱完成注册并登录 4. 登录成功后，压缩文件夹中将显示本数据集 5. 下载并解压该文件夹 6. 可根据研究需求自由使用本数据集（文件夹包含原始图像、标注真值图（groundtruths）以及二值植被掩码。真值标注中，背景标签值为0，作物标签值为1，杂草标签值为2；maskref子文件夹提供用于可视化的标注数据） 更多数据集与已发表论文请参阅相关链接