Although California native bees are key pollinators for fruits and crops, they remain understudied compared to the Western honey bee. Examining morphological traits in native bees, such as pilosity, or hairiness, to understand taxonomic variation among species can inform and support conservation efforts. Three subspecies of long-horned bees, Melissodes tepidus timberlakei, M. tepidus yumensis, and M. tepidus tepida, occupy distinct regions across the western United States (e.g., California, Nevada, Oregon, and Arizona), but their morphological distinctions remain unclear. Pilosity, or hairiness, is a critical trait for thermoregulation, pollen collection, and species recognition, and may offer key insights into subspecies differentiation. I hypothesize that the three Melissodes tepidus subspecies differ significantly in pilosity coverage and lightness as adaptations to their unique ecological niches and climates. To test this hypothesis, I captured, stacked, and analyzed lateral images (both left and right sides) from five specimens per subspecies (totaling 30 images). Using a convolutional neural network explicitly trained for bee hair segmentation, I quantified hair coverage (percentage of hair pixels relative to body surface area) and lightness (numerical value of pixel lightness on a grayscale continuum). ANOVA analyses revealed no statistically significant differences among subspecies in either hair coverage (p = 0.129) or lightness (p = 0.207). Still, visual representations, such as boxplots and swarmplots, demonstrate M. tepidus yumensis to be hairier than M. tepidus tepida and M. tepidus timberlakei. This trend suggests that increasing the sample size may strengthen statistical significance and highlight pilosity differences. In the future, incorporating additional factors, such as sex differentiation, behavioral evidence, and genetic data, will enhance our understanding of subspecies divergence and inform more accurate conservation strategies.