Sustainability is one of the grand challenges of humanity. This urgency has driven alternatives such as using microalgae to capture and transform carbon dioxide (CO 2) into valuable products. Although all biotechnological processes face several challenges such as scaling up, on-line monitoring, and control of critical biological variables (pH, temperature). For photobioreactors other variables such as light intensity which commonly affects growth phenotypes, are critical to have more efficient bioreactor operations while decreasing shading effects. This work describes the design and development of a low-cost monitoring and control system designed to automate the measuring process of meaningful variables in a photobioreactor. The proposed system is based on Raspberry Pi 4 microcomputer programming with the open-source programming language Python. The performance of the system was evaluated in a bubble column over a ten-day course of growth of the algae Haematococcus lacustris. As expected, the cost of this monitoring system was only the 6% of the average potential cost found in the commercial market. The system has other advantages for example that it is portable and can be installed in different types of photobioreactors (airlift, stirred tank, raceway). Moreover, all collected data was visualized in the display and stored for external analysis in compatible formats. When the algae H. lacustris was inoculated and the variables such as pH and volume started to change we found that our system achieved comparable performance to external sensors (with a measurement error of less than 1%) while attaining final biomass densities of up to 1, which is remarkably higher than the obtained in flasks cultures (0.1). Development of reliable low-cost monitoring systems facilitates the expansion of operation to precision and parallel culturing of light-driven biotechnology processes.