3D Reconstruction and Characterization of High-Throughput Cotton Phenotyping Data for Analyzing Growth and Development.

The architecture of many crops significantly affects their grain yield. In this light, the application of high-throughput plant phenotyping methods in an effort to continuously study plant populations under relevant growing conditions creates the means to more efficiently study the genetic basis of dynamic adaptive traits. We propose to employ a field-based high-throughput plant phenotyping robotic system mounted with cameras to simultaneously take measurements throughout the growing season. The trials used for evaluation will be conducted under well-watered and water-limited conditions in replicated field experiments in Lubbock ,TX, with trait measurements taken at different times on multiple days throughout the growing season.

The data collected will be used in the reconstruction of 3D imagery of the plant structure. The 3D imagery will be used to help us identify the differences in structural growth among different cultivars and how these differences affect, for example leaf morphology and boll distribution.

Our goal is to understand and model the structural pattern of the different cultivars under varying environmental conditions and more specifically, how these interactions contribute to physiological, fiber quality, and agronomic traits differences.

The system will be evaluated on fourteen cotton cultivars grown under seven irrigation levels during the 2016, 2017 and 2018 seasons.