A hybrid ACO–random forest optimization framework for scalable microalgae biomass estimation using multispectral imaging

Accurate estimation of algal biomass is essential for monitoring ecosystem productivity, managing aquaculture systems, and optimizing bioresource applications. However, traditional in situ methods are labor-intensive and spatially limited, while remote sensing approaches struggle with nonlinear spectral-biological relationships and the complexity of high-dimensional models. This study develops a hybrid ant colony optimization-random forest regression (ACO-RFR) framework that integrates feature selection with hyperparameter optimization to improve biomass prediction from multispectral imagery. The preprocessing pipeline combined reflectance normalization, multicollinearity screening, and outlier detection to reduce redundancy and noise. The ACO-RFR achieved both feature reduction and robust optimization, yielding high predictive accuracy (R² = 0.96, 95% CI 0.94-0.98; RMSE = 0.05 g L^-1, 95% CI 0.04-0.07) while reducing model dimensionality by more than 60%. Feature importance analysis highlighted NDVI, NIR/red ratios, and texture entropy as key biologically meaningful predictors of chlorophyll-a concentration. By leveraging low-cost imaging and computational efficiency, the framework enables scalable, real-time monitoring for aquaculture, ecological assessments, and biofuel production.