Abstract
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.
Highlights
For optimizing any process, it is important to know or observe the current status of the system to optimize
We presented a novel approach for spatio-temporal registration of 3D point clouds of individual plants
Our approach works as follows: First, it estimates the skeletal structure of the plant, exploiting a point-wise classification approach to compute a skeleton representing the plant. This skeleton structure including the semantic information is used to find reliable correspondences between parts of the plant recorded at different points in time using a novel data association approach that relies on a hidden Markov model
Summary
It is important to know or observe the current status of the system to optimize. Approaches for observing or monitoring dynamic systems over extended periods of time are of key interest in several disciplines, especially when dealing with complex systems where input-output relations are complex to formalize. In plant sciences and modern agriculture high-resolution monitoring of plants plays an important role [1, 2].
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