Effect of Aging on Deposit Characteristics Obtained by Crude Oil Blending

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Automated summary

This article discusses the importance of microalgae species identification and digital microscopy techniques. Microalgae species can be identified through DNA analysis and various microscopy techniques. However, traditional methods have limitations, so researchers have begun to consider digital microscopy techniques. The article proposes innovative ways of combining digital microscopy with hardware and software to achieve reliable microalgae species identification and real-time acquisition. In addition, machine learning and deep learning algorithms are also used for image classification. Overall, the article provides comprehensive insights into the challenges of developing a robust digital classification tool for the future.