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Spatio–temporal determination of phenotypic traits, such as height, leaf angles, and leaf area, is important for the understanding of crop growth and development in modern agriculture and crop science. Measurements of these parameters for individual plants so far have been possible only in greenhouse environments using high-resolution 3D measurement techniques, such as laser scanning or image-based 3D reconstruction. Although aerial and ground-based vehicles equipped with laser scanners and cameras are more and more used in field conditions to perform large-scale phenotyping, these systems usually provide parameters more on the plot level rather than on a single plant or organ level. The reason for this is that the quality of the 3D information generated with those systems is mostly not high enough to reconstruct single plants or plant organs. This paper presents the usage of a robot equipped with a high-resolution mobile laser scanning system. We use the system, which is usually used to create high-definition 3D maps of urban environments, for plant and organ-level morphological phenotyping in agricultural field conditions. The analysis focuses on the point cloud quality as well as the system's potential by defining quality criteria for the point cloud and system and by using them to evaluate the measurements taken in an experimental agricultural field with different crops. Criteria for evaluation are the georeferencing accuracy, point precision, spatial resolution, and point cloud completeness. Additional criteria are the large-scale scan efficiency and the potential for automation. Wind-induced plant jitter that may affect the crop point cloud quality is discussed afterward. To show the system's potential, exemplary phenotypic traits of plant height, leaf area, and leaf angles for different crops are extracted based on the point clouds. The results show a georeferencing accuracy of 1–2 cm, a point precision on crop surfaces of 1–2 mm, and a spatial resolution of just a few millimeters. Point clouds become incomplete in the later stages of growth since the vegetation is denser. Wind-induced plant jitters can lead to distorted crop point clouds depending on wind force and crop size. The phenotypic parameter extraction of leaf area, leaf angles, and plant height from the system's point clouds highlight the outstanding potential for 3D crop phenotyping on the plant-organ level in agricultural fields. [ABSTRACT FROM AUTHOR]

Spatio–temporal determination of phenotypic traits, such as height, leaf angles, and leaf area, is important for the understanding of crop growth and development in modern agriculture and crop science. Measurements of these parameters for individual plants so far have been possible only in greenhouse environments using high-resolution 3D measurement techniques, such as laser scanning or image-based 3D reconstruction. Although aerial and ground-based vehicles equipped with laser scanners and cameras are more and more used in field conditions to perform large-scale phenotyping, these systems usually provide parameters more on the plot level rather than on a single plant or organ level. The reason for this is that the quality of the 3D information generated with those systems is mostly not high enough to reconstruct single plants or plant organs. This paper presents the usage of a robot equipped with a high-resolution mobile laser scanning system. We use the system, which is usually used to create high-definition 3D maps of urban environments, for plant and organ-level morphological phenotyping in agricultural field conditions. The analysis focuses on the point cloud quality as well as the system’s potential by defining quality criteria for the point cloud and system and by using them to evaluate the measurements taken in an experimental agricultural field with different crops. Criteria for evaluation are the georeferencing accuracy, point precision, spatial resolution, and point cloud completeness. Additional criteria are the large-scale scan efficiency and the potential for automation. Wind-induced plant jitter that may affect the crop point cloud quality is discussed afterward. To show the system’s potential, exemplary phenotypic traits of plant height, leaf area, and leaf angles for different crops are extracted based on the point clouds. The results show a georeferencing accuracy of 1–2 cm, a point precision on crop surfaces of 1–2 mm, and a spatial resolution of just a few millimeters. Point clouds become incomplete in the later stages of growth since the vegetation is denser. Wind-induced plant jitters can lead to distorted crop point clouds depending on wind force and crop size. The phenotypic parameter extraction of leaf area, leaf angles, and plant height from the system’s point clouds highlight the outstanding potential for 3D crop phenotyping on the plant-organ level in agricultural fields.