RealWheat2021

The dataset is approximately 230 GB in size, compressed into 12 volumes and stored across 6 Zenodo repositories. A real wheat dataset (RealWheat2021) was constructed for network development and performance evaluation. First, the original UAV images were stitched and geometrically calibrated with the 11 GCPs using the Agisoft Metashape Professional 1.5.1 software (Agisoft LLC, St. Petersburg, Russia). The orthophotos were manually adjusted to the north-south direction, and regions of interest were then clipped using the ArcMap 10.8 software (ESRI, Redlands, USA). A total of ten orthophotos belonging to six growth stages were generated. Second, the ten orthophotos were partitioned into training and testing datasets, respectively, adhering to a ratio of 7:3. Notebally, the testing datasets include three Stages for studying the robustness of proposed methods on different growth stages. Third, the ArcGIS software was employed to label each plot within the orthophotos. Fourth, each orthophoto and its label file were cropped into patches using the ArcMap software. A predefined sliding window approach was employed to extract patches from each orthophoto and its corresponding label file, with each patch having a consistent width and height of 1024 pixels, encompassing approximately four plots. The sliding step of the window was set as 0 to avoid any intersection of each patch. Fifth, the patches were rotated for data augmentation. The patches cropped from the orthophotos were defined as the initial direction (0°). Then, each patch was rotated clockwise starting from 0° to 90° with an interval of 15° (0°, 15°, 30°, 45°, 60°, 75°, 90°) using a Python package named OpenCV. The corresponding labels were processed in the same way as the patches. Finally, the RealWheat2021 dataset was constructed with 115234 images.本数据集总大小约230 GB，被压缩为12个分卷，存储于6个Zenodo仓储中。 本研究构建了真实小麦数据集（RealWheat2021），用于神经网络开发与性能评估。具体构建流程如下： 首先，使用俄罗斯圣彼得堡Agisoft LLC公司的Agisoft Metashape Professional 1.5.1软件，对原始无人机（Unmanned Aerial Vehicle, UAV）影像进行拼接，并通过11个地面控制点（Ground Control Point, GCP）完成几何校正。随后将正射影像手动调整为南北朝向，再使用美国雷德兰兹ESRI公司的ArcMap 10.8软件裁剪出感兴趣区域，最终生成覆盖6个生育期的共10张正射影像。 其次，按照7:3的比例将10张正射影像划分为训练集与测试集；值得注意的是，测试集涵盖3个生育期，用于验证所提方法在不同生育期下的鲁棒性。 第三，使用ArcGIS软件为正射影像中的每个试验小区添加标注。 第四，借助ArcMap软件将每张正射影像及其标注文件裁剪为图像块（patch）：采用预设的滑动窗口法从正射影像与对应标注文件中提取图像块，每个图像块的宽高均为1024像素，可覆盖约4个试验小区；滑动步长设为0，以避免各图像块间出现重叠。 第五，通过旋转图像块完成数据增强：将从正射影像裁剪得到的图像块定义为初始方向（0°），随后使用Python的OpenCV库将每张图像块以15°为间隔，从0°顺时针旋转至90°（涵盖0°、15°、30°、45°、60°、75°、90°共7个角度）；对应的标注文件采用与图像块一致的旋转方式进行处理。 最终，RealWheat2021数据集共包含115234张图像。