The primary method used to audit honey bee (Apis mellifera Linnaeus, 1758 [Hymenoptera: Apidae]) colony strength for almond pollination services, Nasr et al.'s (1990) frame-top cluster count method, is a subjective visual audit that relies on an auditor's spot assessment and may lack rigor and repeatability. We created novel, open-source software for the analysis of frame-top cluster count photographic assessments to improve methodological rigor and repeatability. We evaluated 2 existing visual audit methods, created 3 novel audit method variations, and determined between-method conversion factors using linear modeling. The software has potential applications in apiological research, apiarist and orchardist colony auditing, as well as training future generations of apiarists in auditing techniques. The software enhances the rigor and repeatability of Nasr et al.'s (1990) frame-top cluster count population assessment. In this article, we introduce the novel open-source software and between-method regression equations and review the tested visual assessment methods and their application.