LiDAR platform for acquisition of 3d plant phenotyping database

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%.

• Autores: Comeche, José Manuel; Murcia, Harold F.; Méndez, Dehyro; Martínez Pérez, Juan Francisco
• Tipo de recurso: Article of journal
• Fecha de publicación: 2022
• Institución: Universidad de Ibagué
• Repositorio: Repositorio Universidad de Ibagué
• Idioma: eng
• OAI Identifier: oai:repositorio.unibague.edu.co:20.500.12313/3851
• Acceso en línea: https://hdl.handle.net/20.500.12313/3851
• Palabra clave: 3D maize database; 3D reconstruction; LiDAR platform; Plant phenotyping; Point clouds
• Rights: openAccess
• License: http://purl.org/coar/access_right/c_abf2