Forero Manuel G, Murcia Harold F, Méndez Dehyro, Betancourt-Lozano Juan
Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia.
Plants (Basel). 2022 Aug 25;11(17):2199. doi: 10.3390/plants11172199.
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
目前，还没有用于幼苗表型分析的3D点云与图像免费数据库。因此，本文介绍了一个使用3D激光雷达进行幼苗扫描的平台，利用该平台获取了一个用于植物表型研究的数据库。总共使用一台RGB相机和一台SICK LMS4121R - 13000激光扫描仪记录了362株玉米幼苗，其角分辨率分别为45°和0.5°。扫描的植株具有多样性，幼苗捕获高度从小于10厘米到40厘米不等，且在室内不同光照条件下种植后7至24天不等。对这些点云进行了处理，以去除噪声和瑕疵，平均绝对精度误差为0.03厘米，与图像同步并添加了时间戳。该数据库包括原始数据和处理后的数据以及手动标注的茎和叶标签。作为数据库应用的一个示例，采用随机森林分类器基于形态学描述符识别幼苗部分，准确率为89.41%。
