The problem of automated bacterial colony counting is a very relevant one, due to the high importance of bacteriological analysis. Moreover, this automated counting saves biologists time and improves the accuracy of their experiments. This paper has two aims: to investigate the challenges of automated bacterial colony counting, and to address the joint challenges of petri dish localization and bacterial colony reflections in such dishes. These reflections can seriously reduce the accuracy of automated bacterial colony counting. Therefore, the main aim of this paper is to show new methods for detecting and removing bacterial colony reflections in a petri dish by the use of computer vision. It also proposes new methods for petri dish localization and the digital removal of bacterial colony reflections. Additionally, these methods can be implemented on a mobile platform, such as Android and Raspberry Pi. The experimental part of the paper contains the results, and descriptions of petri dish localization, and detecting and removing bacterial colony reflections. The proposed methods and the data obtained from these experiments significantly improve the accuracy of automated bacterial colony counting.