Fuzzy clustering of maize plant-height patterns using time series of UAV remote-sensing images and variety traits

Abstract

The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.