Recognition and Biomass Prediction of Microalgae Based on Convolutional Neural Network Algorithms
The biomass yield of microalgae culture directly determines its energy utilization efficiency.Traditional biomass measurement relies on offline manual detection and analysis,inevitably resulting in significant labor wastage and time costs.A detection method that combines image analysis with microalgae cultivation is introduced.Utilizing three deep convolutional neural network models—ResNet,MobileNet,and EfficientNet—the method enables the online identification of algae species and directly fits a nonlinear mapping between microalgae images and concentration to predict microalgal biomass accurately.The study demonstrates that the classification accuracy of these three models for three experimental algae species(Chlorella,Rhodophyta,and Spirulina)exceeds 99%.Rhodophyta,owing to its color characteristics,exhibits the best predictive performance.ResNet showed the optimal performance in predicting algal biomass,with the determination coefficients(R2)for the biomass of the three algae being 0.766 4,0.962 8,and 0.921 5,respectively.This method essentially meets the monitoring requirements for algal biomass during microalgae cultivation and provides a highly promising technical solution for the industrial process monitoring of algae energy conversion.