The goal of this study is to classify microalgae of different species such as Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis, using machine learning (ML) and deep learning (DL) methods

The findings in the present study will be a breakthrough for the estimation of CPC concentration from S. platensis solely based on the information provided in the image without the need to perform a prior extraction process and identification of CPC concentration using analytical equipment.

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and