Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms

Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms (HABs) to enable efficient response by water management agencies. This manuscript presents an image processing pipeline developed to adapt ARTiMiS, a low-cost automated flow-imaging device, for real-time algal monitoring in natural freshwater systems. This pipeline addresses several challenges associated with autonomous imaging of aquatic samples, such as flow-imaging artifacts (i.e., out-of-focus and background objects), as well as strategies to efficiently identify novel objects that are not represented in the training data set; the latter is a common challenge with the application of deep learning approaches for image classification in environmental systems. The pipeline leverages a random forest model to identify out-of-focus particles with an accuracy of 89% and a custom background particle detection algorithm to identify and remove particles that erroneously appear in consecutive images with >97 ± 2.8% accuracy. Furthermore, a convolutional neural network (CNN), trained to classify taxonomical classes, achieved 95% accuracy in a closed set classification. Nonetheless, the supervised closed-set classifiers struggled with the accurate classification of objects when challenged with novel particles, which are common in complex natural environments; this limits real-time monitoring applications by requiring extensive manual oversight. To mitigate this, three methods incorporating classification with rejection were tested to improve model precision by flagging irrelevant or unknown classes. Combined, these advances present a fully integrated, end-to-end solution for real-time HAB monitoring in natural freshwater systems, which enhances the scalability of automated detection in dynamic aquatic environments.