Online monitoring of Haematococcus lacustris cell cycle using machine and deep learning techniques

[Display omitted] •Trained and validated two artificial intelligence models for image classification.•Automated system classifies four Haematococcus cell cycle stages.•Identified key system requirements through model explainability using SHAP.•Successful application to a dynamic photobioreactor culture. Optimal control and process optimization of astaxanthin production from Haematococcuslacustris is directly linked to its complex cell cycle ranging from vegetative green cells to astaxanthin-rich cysts. This study developed an automated online monitoring system classifying four different cell cycle stages using a scanning microscope. Decision-tree based machine learning and deep learning convolutional neural network algorithms were developed, validated, and evaluated. SHapley Additive exPlanations was used to examine the most important system requirements for accurate image classification. The models achieved accuracies on unseen data of 92.4 and 90.9%, respectively. Furthermore, both models were applied to a photobioreactor culturing H.lacustris, effectively monitoring the transition from a green culture in the exponential growth phase to a stationary red culture. Therefore, online image analysis using artificial intelligence models has great potential for process optimization and as a data-driven decision support tool during microalgae cultivation.