Microalgae-based wastewater treatment has resulted in a paradigm shift toward nutrient removal and simultaneous resource recovery. However, traditionally used microalgal biomass quantification methods are time-consuming and costly, limiting their large-scale use. The aim of this study is to develop a simple and cost-effective image-based method for microalgae quantification, replacing cumbersome traditional techniques. In this study, preprocessed microalgae images and associated optical density data were utilized as inputs. Three feature extraction methods were compared alongside eight machine learning (ML) models, including linear regression (LR), random forest (RF), AdaBoost, gradient boosting (GB), and various neural networks. Among these algorithms, LR with principal component analysis achieved an R2 value of 0.97 with the lowest error of 0.039. Combining image analysis and ML removes the need for expensive equipment in microalgae quantification. Sensitivity analysis was performed by varying the train-test splitting ratio. Training time was included in the evaluation, and accounting for energy consumption in the study leads to the achievement of high model performance and energy-efficient ML model utilization. © 2024 American Chemical Society.
