A Low-Cost Efficient System for Monitoring Microalgae Density Using Gaussian Process

This article presents a low-cost system for efficiently monitoring the density of microalgae in a closed cultivation system, such as a photobioreactor. In fact, microalgal density can be accurately determined by manually counting methods, such as the direct microscopic count technique. However, the manual approaches are cumbersome, time-consuming, and impractical to be implemented in a closed cultivation system. Therefore, in the proposed monitoring system, microalgae are first proposed to be pumped from a culturing tank into a sample container placed inside a dark box. A low-cost camera is utilized to capture images of microalgae through the transparent sample container under artificial light. It is then proposed to represent microalgal density through two average pixel values of red and green color channels of the corresponding image. Moreover, the Gaussian process (GP) is exploited to statistically learn a data-driven model of microalgae density given the measured images. The learned model can then be used to effectively predict the density of microalgae where only their corresponding image data are required. The proposed approach was evaluated in a real-world closed bioreactor system of culturing Chlorella vulgaris microalgae, where the model was trained by 100 images selected randomly from 125 ones. In 10000 random runs, the accuracy of the estimated density results is about 8.6% (+/- l.8%).

Automated summary

This article presents a low-cost system for efficiently monitoring microalgae density in a closed cultivation system using image capture and statistical learning to accurately predict microalgae density.