An automated pipeline for supervised classification of petal color from citizen science photographs

Abstract

Premise: Petal color is an ecologically important trait, and uncovering color variation over a geographic range, particularly in species with large distributions and/or short bloom times, requires extensive fieldwork. We have developed an alternative method that segments images from citizen science repositories using Python and k-means clustering in the hue-saturation-value (HSV) color space. Methods: Our method uses k-means clustering to aggregate like-color pixels in sample images to generate the HSV color space encapsulating the color range of petals. Using the HSV values, our method isolates photographs containing clusters in that range and bins them into a classification scheme based on user-defined categories. Results: We demonstrate the application of this method using two species: one with a continuous range of variation of pink-purple petals in Geranium maculatum, and one with a binary classification of white versus blue in Linanthus parryae. We demonstrate results that are repeatable and accurate. Discussion: This method provides a flexible, robust, and easily adjustable approach for the classification of color images from citizen science repositories. By using color to classify images, this pipeline sidesteps many of the issues encountered using more traditional computer vision applications. This approach provides a tool for making use of large citizen scientist data sets.