Morphological response of Arthrospira platensis to cultivation conditions using a microfluidic microscope

Arthrospira platensis is a filamentous cyanobacterium valued for its high protein content and biotechnological versatility. This study explores the influence of key environmental parameters—temperature, pH, irradiance, and agitation—on both the morphological characteristics and biomass productivity of A. platensis . Using Microdeep™, a novel AI-integrated microfluidic microscope, filament traits such as length, diameter, and structural integrity were quantified alongside dry mass equivalent (DME), serving as a proxy for biomass concentration. Optimal growth conditions were identified at 40 °C, pH 9–10, and an irradiance of 200 μmol·m −2 ·s −1 , under which cultures exhibited maximal filament elongation and biomass yield without structural degradation. Across experimental conditions, filament lengths ranged from 30 to 420 μm and diameters from 6 to 11 μm, highlighting dynamic morphological plasticity. Under adverse conditions—extreme pH (6 or 11), high irradiance (2200 μmol·m −2 ·s −1 ), or non-optimal temperatures (10 or 45 °C)—filaments experienced fragmentation, reduced elongation, and biomass decline, primarily driven by stress-induced necridia formation and photodamage. The Microdeep™ system demonstrated a high degree of accuracy in biomass estimation, with dry matter equivalent measurements showing a linear correlation to gravimetric dry weight (R 2 > 0.99). This confirms its suitability for real-time cultivation monitoring and opens avenues for precision-controlled photobioreactor operations. Overall, the study reinforces the utility of AI-enhanced imaging tools in optimizing microalgal bioprocesses through detailed, quantitative analysis of morphological and physiological traits. • A novel AI-integrated microfluidic system enables real-time monitoring of Arthrospira platensis morphology and biomass concentration. • Filament morphology responds sensitively to temperature, pH, and irradiance; extremes induce fragmentation and reduce productivity. • Dry mass equivalent measurements strongly correlate with gravimetric biomass (R 2 > 0.99), validating image-based quantification. • This method supports automated, non-invasive cultivation control in algal photobioreactors, enhancing precision bioprocessing.