Abstract
The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.
Highlights
The three-dimensional (3D) architecture of a plant affects its ability to compete for resources, such as light and space aboveground as well as water and nutrients belowground [1]
The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models
Insertion angle decreased at higher leaf positions and curvature increased with increasing plant age
Summary
The three-dimensional (3D) architecture of a plant affects its ability to compete for resources, such as light and space aboveground as well as water and nutrients belowground [1]. Plants perceive neighbor-associated changes in light quality and quantity mainly with phytochromes for red and far-red light and cryptochromes and phototropins for blue light [3]. Most of the knowledge on plant photomorphogenesis and photobiology have been gained through experiments in controlled environments. Application of this knowledge for the precision management and engineering of crops under field conditions requires characterization of the light quality signal (blue or red and far-red light) perceived by plant organs within a canopy and quantification of the subsequent responses of plant organs in a stand setting [2,7]. Especially functional–structural modeling, has been considered an important tool in addressing these challenges [7]
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