Beekeeping is one of the most important activities for humans. Since ancient times, honey has been used in the treatment of several diseases and is an extremely powerful antioxidant. The process of visual analysis of the apiary requires trained specialists who try to obtain relevant information to make a decision about what to do with the honeycomb. Since the process is performed manually, given the complexity of the task, opportunities arise for the application of automated systems that can assist the beekeeper's decision making. Thus, this paper presents the development of the application \textit{SegBee}, a computational tool that performs the segmentation in the apiary plates, where there is the presence of honey, in an accessible, fast and practical way. To do this, the OpenCV library was used for the digital image processing part, and the Kivy library was used to develop the interface of the mobile application. The tests performed showed that the images were adequately segmented by \textit{SegBee}, indicating where the honey is located on each analyzed plate. A visual comparison was made between results obtained by \textit{SegBee} and another commercial application, demonstrating the effectiveness of the developed tool. The proposed solution contributes to the improvement of the beekeeping professionals' work, once the application is simple to use and fast to process, being able to help in the honey identification task in apiaries plates.