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 o

SUBMITTER: Perez-Udell RA 

PROVIDER: S-EPMC9934523 | biostudies-literature | 2023 Jan-Feb

REPOSITORIES: biostudies-literature