Deep learning-based classification of microalgae using light and scanning electron microscopy images

Abstract

Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore, the accurate detection, monitoring, identification, and tracking of algae are imperative, albeit demanding considerable time, effort, and expertise, as well as financial resources. Deep learning, employing image pattern recognition, emerges as a practical and promising approach for rapid and precise microalgae cell counting and identification. In this study, we processed light microscopy (LM) and scanning electron microscopy (SEM) images of two Cyanobacteria species and three Chlorophyta species to classify them, utilizing state-of-the-art Convolutional Neural Network (CNN) models, including VGG16, MobileNet V2, Xception, NasnetMobile, and EfficientNetV2. In contrast to prior deep learning based identification studies limited to LM images, we, for the first time, incorporated SEM images of microalgae in our analysis. Both LM and SEM microalgae images achieved an exceptional classification accuracy of 99%, representing the highest accuracy attained by the VGG16 and EfficientNetV2 models to date. While NasnetMobile exhibited the lowest accuracy of 87% with SEM images, the remaining models achieved classification accuracies surpassing 93%. Notably, the VGG16 and EfficientNetV2 models achieved the highest accuracy of 99%. Intriguingly, our findings indicate that algal identification using optical microscopes, which are more cost-effective, outperformed electron microscopy techniques.