On the characterization of flowering curves using Gaussian mixture models

TL;DR

This paper introduces a statistical approach using Gaussian mixture models to characterize flowering curves, analyze reblooming, and classify rosebush flowering patterns, providing insights into biological control factors.

Contribution

The study develops a novel methodology combining Gaussian mixture models, a new selection criterion, and clustering techniques to analyze flowering curves and reblooming properties.

Findings

Reblooming is uncorrelated with flowering precocity.

A new selection criterion accounts for asymmetry in flowering curves.

Classifications reveal distinct flowering and reblooming patterns.

Abstract

In this paper, we develop a statistical methodology applied to the characterization of flowering curves using Gaussian mixture models. Our study relies on a set of rosebushes flowering data, and Gaussian mixture models are mainly used to quantify the reblooming properties of each one. In this regard, we also suggest our own selection criterion to take into account the lack of symmetry of most of the flowering curves. Three classes are created on the basis of a principal component analysis conducted on a set of reblooming indicators, and a subclassification is made using a longitudinal $k$--means algorithm which also highlights the role played by the precocity of the flowering. In this way, we obtain an overview of the correlations between the features we decided to retain on each curve. In particular, results suggest the lack of correlation between reblooming and flowering precocity. The pertinent indicators obtained in this study will be a first step towards the comprehension of the environmental and genetic control of these biological processes.