Efficient Microalgae Species Identification using Compact Convolutional Neural Network

Abstract

In this study, we propose a novel approach for microscopic algae species classification by implementing a compact Convolutional Neural Network (CNN) model. Our methodology was tested on a diverse dataset consisting of 18 distinct species of microscopic algae, demonstrating a remarkable classification accuracy exceeding 99%. The outstanding performance of this model is attributed to its compact architecture which maintains high precision while minimizing computational resources, making it a feasible option for real-time applications. Furthermore, we incorporated advanced data augmentation techniques to enhance the generalization capability of our model. By artificially expanding the training dataset, we effectively increased the model's robustness to variance in input data, which significantly contributed to the model's high classification accuracy. The research findings underscore the potential of compact CNN models coupled with data augmentation strategies in high-precision microscopic algae classification tasks, paving the way for future innovations in the field of aquatic microbiology and environmental monitoring.