Traditional methods, such as cell counting and dry cell weight measurement, are frequently employed for measuring microalgal density. However, these methods encounter challenges including low efficiency, dependence on the operator’s experience, and the potential for sample contamination. In this study, a luminance-tunable white-light panel LED luminarie is used to capture images of Nannochloropsis sp. and Chaetoceros sp. samples, effectively reducing interferences from the environmental background. Then, the average pixel color and the Euclidean distance from the standard white color of images are calculated to determine the color difference relative to the standard white. An arctan inverse tangent function based fitting model is developed based on the calculated color difference and microalgal density, which is subsequently used to estimate the unknown microalgal density. Compared to the traditional cell counting method, the proposed method captures images with greater uniformity and higher resistance to interference, achieving an average accuracy of over 91% in measuring microalgal density. In addition to its direct application in microalgal cultivation, the proposed method shows promise for application in water quality monitoring, early warning of algal blooms, and early detection of marine red tides.