Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision

Abstract

Highlights: What are the main findings? This study presents a low-cost, automated method for estimating microalgae cell concentration using classical computer vision techniques, achieving a Pearson’s correlation coefficient of 0.96 compared to manual counts. The proposed approach processes images in under 30 s, offering interpretability and adaptability for laboratories with limited resources. What are the implications of the main findings? This method bridges the gap between manual counting and expensive automated systems, making cell concentration estimation accessible for academic and research settings. It provides a scalable solution for hydrochemistry, biofuel production, and ecological studies, with potential applications in other microbiological fields. Accurate and efficient estimation of microalgae cell concentration is critical for applications in hydrochemical monitoring, biofuel production, pharmaceuticals, and ecological studies. Traditional methods, such as manual counting with a hemocytometer, are time-consuming and prone to human error, while automated systems are often costly and require extensive training data. This paper presents a low-cost, automated approach for estimating cell concentration in Chlorella vulgaris suspensions using classical computer vision techniques. The proposed method eliminates the need for deep learning by leveraging the Hough circle transform to detect and count cells in microscope images, combined with a conversion factor to translate pixel measurements into metric units for direct concentration calculation (cells/mL). Validation against manual hemocytometer counts demonstrated strong agreement, with a Pearson correlation coefficient of 0.96 and a mean percentage difference of 17.96%. The system achieves rapid processing (under 30 s per image) and offers interpretability, allowing specialists to verify results visually. Key advantages include affordability, minimal hardware requirements, and adaptability to other microbiological applications. Limitations, such as sensitivity to cell clumping and impurities, are discussed. This work provides a practical, accessible solution for laboratories lacking expensive automated equipment, bridging the gap between manual methods and high-end technologies.