Automatic Yeast Detection and Counting Using Computer Vision Techniques

Abstract

This paper presents the development of a computer vision system that automatically identifies and counts viable and inviable brewer's yeast, to improve the time and accuracy of results obtained compared to the manual expert counting method commonly performed in the brewing industry. The equipment used consists of a digital video camera coupled to an optical microscope, which transmits the captured images, in real time, to the computer. Two approaches were tested and implemented, one taking into account the morphology and color of yeasts, and the other using machine learning. Although there are programs that automatically count yeasts, this is the first application that makes use of convolutional neural network techniques with Yolo to identify yeasts, making the results more accurate and reliable compared to manual methods. Experiments were carried out to measure the performance and accuracy of the prototype, which are presented in this article.