Generation and Application of Hyperspectral 3D Plant Models

Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they have been also used for close-range sensing of plant canopies with a more complex architecture. The complex geometry of plants and their interaction with the illumination scenario severely affect the spectral information obtained. The combination of hyperspectral images and 3D point clouds are a promising approach to face this problem. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified an modeled. Reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential potential to improve automated phenotyping significantly.

We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. The reliable and accurate generation requires the adaptation of methods designed for man-made scenes. The adaption requires new types of point descriptors and 3D matching technologies. Also the application and analysis of 3D plant models creates new challenges as the light scattering at plant tissue is highly complex and so far not fully described. New approaches for measuring, simulating, and visualizing light fluxes are required for improved sensing and new insights into stress reactions of plants.