Plant Event Detection from Time-Varying Point Clouds

Studying the growth dynamics of developing plants is of critical importance in plant sciences. The traditional methods rely on either manual measurement, which involves tedious labor work, or 2D image-based approaches, which cannot fully characterize plants in 3D. Given the advances of scanners and 3D reconstruction methods, scientists begin to pay more attention to 3D models to improve accuracy. However, existing methods mostly focus on the growth of a whole plant rather than its detailed substructures. In this paper, we have developed an end-to-end pipeline to detect the key events on both the whole plant and the specific components. Our method is achieved by building 3D models from images, segmenting individual components, and capturing traits. We implement an experiment on maizes for evaluation and successfully detect events in the process of growth.