Light intensity is a very important factor that determines the growth of photosynthetic cells. In this study, the light distribution in a photobioreactor was analyzed by processing the images captured with a digital camera. The contour images obtained by filtering the original images clearly showed the effects of the cell concentration and external light intensity on the light distribution. Image-processing techniques were then applied to predict the cell density in the photobioreactor. To correlate the cell concentration with the light intensity in the photobioreactor, the captured images were processed using two different approaches. The first method involved the use of an average gray value after deriving a simplified model equation that could be related to the cell density. The second method involved the use of local points instead of a representative value. In this case, an artificial neural network model was adopted to infer the cell density from the information of the local points. By using these two methods, it was possible to relate the image data to the cell concentration. Finally, we compared these two methods with regard to their accuracy, easiness, and effectiveness.