AI-Assisted Plate Counting for Accurate OD600–CFU Calibration in Some Wheat Rhizosphere-Related Pseudomonas fluorescens Isolates

Abstract

Optical density at 600 nm (OD600) is widely used to estimate bacterial cell density, yet the conversion of OD600 values to viable cell counts (CFU.mL⁻¹) is often based on generic factors that ignore strain-level variability. This practice can introduce substantial quantitative error, particularly in plant–microbe interaction studies where precise inoculum standardization is essential. In this study, we investigated the strain-specific relationship between OD600 and CFU.mL⁻¹ in five environmental isolates of Pseudomonas fluorescens using an AI-assisted colony counting approach. Serial dilutions covering a wide OD600 range were plated and enumerated using a semi-automated image analysis pipeline with manual verification. For each isolate, calibration curves were constructed by relating OD600 to log10-transformed CFU.mL⁻¹ values. Model fitting showed that a logarithmic function provided the best description of the OD–CFU relationship across the tested range (R² = 0.88–0.97). When OD600 values corresponding to the 0.5 McFarland standard (0.08) were applied to isolate-specific models, predicted viable counts varied by up to ~4.2-fold among isolates. One-way ANOVA confirmed a highly significant isolate effect on CFU estimates. These findings demonstrate that OD600–CFU relationships are not conserved even within a single bacterial species and that reliance on generic conversion factors can lead to substantial over- or underestimation of viable cell numbers. The AI-assisted workflow enabled the generation of dense, statistically robust calibration datasets that would be impractical with manual counting alone. We conclude that isolate- and instrument-specific OD–CFU calibration should be considered a methodological requirement for quantitative microbiology, particularly in plant science, biocontrol, and microbial ecology studies.