A Low-Cost Portable Machine Vision System for Non-Destructive Density Estimation of Dunaliella sp. Cultures

Routine microalgal cell counting requires sampling and microscopy. We developed a low-cost portable machine-vision system (LPMVS) for non-destructive density estimation of visibly green Dunaliella sp. cultures. The compact benchtop LPMVS combines motorized flask loading, a light-isolated dark box with diffuse light-emitting diode (LED) illumination, fixed camera–flask geometry, red–green–blue (RGB) imaging through the flask, mask-derived liquid-region extraction, Raspberry Pi inference, and a local liquid-crystal display (LCD). A ready-state load-to-unload cycle required approximately 37 s, including 6 s of processing. The two 40-day flask trajectories yielded 240 image–hemocytometer pairs; 200 records were used to compare three regressors with 20 features selected from 339 descriptors. K-nearest neighbors (KNN) performed best in the flask-mixed record-level comparison (R2 = 0.98; mean absolute percentage error (MAPE) = 6.41%; n=40). Reciprocal leave-one-flask-out transfer analysis on complete trajectories with independently retrained pipelines gave KNN R2 values of 0.914 and 0.803. In the reverse direction, R2 was 0.936 within the overlapping range (n=79), but 41 above-range densities were systematically underestimated. Bootstrap resampling identified recurrent color-based features in both trajectories. An edge-device check using 10 additional measurements yielded preliminary reference-density tracking (R2 = 0.94). These findings support the feasibility of the LPMVS workflow for non-destructive monitoring under controlled laboratory conditions, but extrapolation and generalization to independent batches and optical configurations remain unestablished.