Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Chong, Jun Wei Roy; Khoo, Kuan Shiong; Chew, Kit Wayne; Ting, Huong-Yong; Show, Pau Loke

doi:10.1016/j.biotechadv.2023.108095

Cited by 28 publications

(19 citation statements)

References 115 publications

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“…59, and the critical assessment of ML‐based microalgae classification research by Chong et al. 60. These developments facilitate the incorporation of AI into the biochemical industry, thereby reducing analytical time, cost, and personnel.…”

Section: Applying Artificial Intelligence To Algae Cultivation and Al...mentioning

confidence: 99%

“…59, and Chong et al. 60 indicate that certain limitations persist in the employed methodologies, such as the trade‐off between precision and complexity, high capital expenditures, and reliance on preprocessing techniques. Furthermore, natural variations in microalgae species and overlapping species can cause misclassification, whereas sparse datasets can lead to underfitting or overfitting models.…”

Section: Applying Artificial Intelligence To Algae Cultivation and Al...mentioning

confidence: 99%

“…This can further potentially enhance the sustainability and profitability of algal bioplastic production. Recent studies have reported advancements in the identification, classification, and cultivation of microalgae species using AI-based systems and ML algorithms, contributing significantly to the bioplastic and algae fields [57][58][59][60]. ML, a subset of AI, has proved to be an effective method for enhancing microalgae farming techniques.…”

Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

“…Chong et al [60] provided an exhaustive review of ML applications in microalgae species identification using digital microscopy techniques. Since 2012, the paper has highlighted the advancements in deep learning, computational capacity, advanced algorithms, and classification challenges.…”

Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

“…ML and IoT technologies enable intelligent farming practices, such as real-time monitoring, remote control, and predictive modeling, for decision-making, thereby increasing crop yield, energy efficiency, and cost savings [49][50][51]. By identifying, classifying, and cultivating microalgae species, ML algorithms have also made significant contributions to the bioplastic and algae disciplines [57][58][59][60]. Despite these obstacles, incorporating ML and IoT into algal bioplastic production can revolutionize the circular economy and create a more sustainable future.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

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Section: Applying Artificial Intelligence To Algae Cultivation and Al...mentioning

confidence: 99%

Section: Applying Artificial Intelligence To Algae Cultivation and Al...mentioning

confidence: 99%

Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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Space‐Efficient 3D Microalgae Farming with Optimized Resource Utilization for Regenerative Food

Liu,

Yu,

Liu

et al. 2024

Advanced Materials

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Photosynthetic microalgae produce valuable metabolites and are a source of sustainable food that supports life without compromising arable land. However, the light self‐shading, excessive water supply, and insufficient space utilization in microalgae farming have limited its potential in the inland areas most in need of regenerative food solutions. Herein, we develop a 3D polysaccharide‐based hydrogel scaffold for vertically farming microalgae without needing liquid media. This liquid‐free strategy is compatible with diverse microalgal species and enables the design of living microalgal frameworks with customizable architectures that enhance light and water utilization. This approach significantly increases microalgae yield per unit water consumption, with an 8.8‐fold increase compared to traditional methods. Furthermore, the dehydrated hydrogels demonstrate a reduced size and weight (∼70% reduction), but readily recover their vitality upon rehydration. Importantly, valuable natural products can be produced in our system including proteins, carbohydrates, lipids, and carotenoids. This study streamlines microalgae regenerative farming for low‐carbon biomanufacturing by minimizing light self‐shading, relieving water supply, and reducing physical footprints, and democratizing access to efficient aquatic food production.This article is protected by copyright. All rights reserved

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Machine Learning and Artificial Intelligence for Algal Cultivation, Harvesting Techniques, Wastewater Treatment, Nutrient Recovery, and Biofuel Production and Optimization

Mafat,

Palla,

Surya

2024

Value Added Products From Bioalgae Based Biorefineries: Opportunities and Challenges

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Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Cited by 28 publications

References 115 publications

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Space‐Efficient 3D Microalgae Farming with Optimized Resource Utilization for Regenerative Food

Machine Learning and Artificial Intelligence for Algal Cultivation, Harvesting Techniques, Wastewater Treatment, Nutrient Recovery, and Biofuel Production and Optimization

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