In Biotechnology advances
Identification of microalgae species is of great importance due to the uprising of Harmful Algae Blooms (HABs), affecting both the aquatic habitat and human health. On the contrary, microalgae have been identified as future green biomass and alternatives due to their promising bioactive compounds activities 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 procedure has encouraged researchers to consider alternate ways due to several limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. Nonetheless, this review highlights the potential and innovation of digital microscopy with the incorporation of both hardware and software that could produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection for generating high image quality by removing unwanted artifacts and noise from the background. The identification of microalgae species is then followed by robust and reliable image classification machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review paper aims to explore and provide comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to develop a robust digital classification tool in near future.
Chong Jun Wei Roy, Khoo Kuan Shiong, Chew Kit Wayne, Ting Huong-Yong, Show Pau Loke
2023-Jan-03
Identification, Image processing, Machine learning, Microalgae, Microscopy
