Abstract
As more people become connected to the grid and the energy demand continues to increase, non-renewable energy sources are being consumed at a faster rate than they are being replaced. Microalgae, often referred to as "green gold" have shown great potential as a renewable energy source due to their unique characteristics. However, not all microalgae are suitable to use as replacements for traditional fossil fuels. Therefore, it is essential for researchers to accurately identify and classify microalgae based on their species and energy-producing capabilities. The main objective of this paper is to present a detailed summary of the current technologies employed in the classification and detection of microalgae. Both traditional manual microscopy and advanced artificial intelligence techniques such as machine learning and deep learning are covered in this overview. Furthermore, this paper offers a critical analysis of these technologies and provides suggestions for enhancing their effectiveness. Despite deep learning being the most advanced technology for microalgae classification and detection, there is still significant potential for future improvements that could further increase the accuracy.
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