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Review

Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation

by
Vijay Rayamajhi
1,†,
Mudasir Hussain
1,†,
Hyunwoung Shin
1,2 and
Sangmok Jung
3,*
1
Department of Biology, Soonchunhyang University, Asan 31538, Chungcheongnam-do, Republic of Korea
2
AlgaeBio, Inc., Asan 31459, Chungcheongnam-do, Republic of Korea
3
Research Institute for Basic Science, Soonchunhyang University, Asan 31538, Chungcheongnam-do, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Processes 2025, 13(12), 3764; https://doi.org/10.3390/pr13123764
Submission received: 4 November 2025 / Revised: 19 November 2025 / Accepted: 20 November 2025 / Published: 21 November 2025
(This article belongs to the Section AI-Enabled Process Engineering)
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Abstract

Microalgae are unicellular, industrially important organisms that are used extensively in a range of industrial, environmental, and biorefinery applications. They can produce lipids, carbohydrates, and possibly additional vital bioactive substances. The increasing popularity of artificial intelligence (AI) in microalgae research can be attributed to its algorithms’ ability to manage the complexity of unexpected biosystems. In the case of microalgae-based biorefineries, AI technology can also help uncover system dynamics and uncertainties, provide helpful predictive analytics, and expedite the optimisation process. AI is used in microalgal cultivation to optimise carbon capture, biomass production, and conditions for growth. Additionally, it is employed for genome editing, automated monitoring, and lipid accumulation enhancement. However, its uses are broad and constantly growing. Furthermore, critical environmental parameters in microalgae culture, including temperature, light intensity, pH, dissolved oxygen, and nutrient levels, may be continually monitored and controlled by internet of things (IoT)-based devices. This review comprehensively summarises the latest applications of AI technology in the field of microalgae cultivation and the role of IoT-based automatic control.

1. Introduction

Microalgae are unicellular, industrially significant organisms that have the ability to produce lipids, carbohydrates, and possibly various additional vital compounds through photosynthesis from carbon dioxide, sunlight, and nutrients [1,2]. Microalgae have certain advantages over other commonly employed microorganisms in industrial production. In contrast to yeast or bacteria, microalgae grow in a medium with very few nutrient requirements and do not depend on high-quality, energy-rich carbon sources like sugars or proteins. They continue to grow quickly while producing sugar and releasing O2 into the environment using CO2 and solar energy [3,4,5,6]. Regulations govern the production and sale of algae-based goods. Only a small number of microalgae species are therefore considered “generally recognised as safe” (GRAS), granting them direct permission for consumption by humans. All other product approval procedures generally turn out to be complicated and longer, particularly for novel algae species or products [6]. Microalgae are the foundation of the aquatic food chain and provide half of the world’s oxygen supply due to their well-balanced lipid and amino acid compositions [7,8,9,10]. In addition to being nontoxic and having great potential for reducing CO2, microalgae are a sustainable energy source [7,11]. Species like Chlorella and Spirulina, which may exhibit protein levels above 51% of their dry mass, much greater than the 30–40% protein found in soybeans, are frequently the focus of commercial production [7,12,13]. Certain species, such as Dunaliella and Haematococcus, are rich in antioxidant carotenoids [7,14]. Under situations of extreme light stress or nitrogen deficiency, certain strains, including Nannochloropsis gaditana and Chlorella vulgaris, have been shown to accumulate lipids up to 50–70% of their dry weight [15,16]. Transesterification of these lipids can produce biodiesel, a green energy source that does not compete with food crops. Apart from biodiesel, catalytic hydroprocessing can also convert microalgal lipids into green hydrocarbons, providing a route to drop-in fuels that are compatible with current infrastructure [15,17]. Microalgae are widely utilised in a variety of industrial, environmental, and biorefinery applications as a result of these exceptional qualities [7,12]. It is anticipated that the global market for products based on microalgae will increase from $32.60 billion in 2017 to $53.43 billion by 2026 [7,18,19].
Artificial intelligence (AI) refers to the ability of machines to simulate human intelligence, enabling them to perform sophisticated tasks like object identification, decision-making, and problem-solving [7,20,21]. The use of AI in microalgae research is growing in popularity because its algorithms can handle the complexity of unpredictable biosystems [2,7]. Researchers can more precisely optimise cultivation conditions and improve the identification, classification, and quantification of different algae strains and their growth patterns by utilising AI [7,18,22]. The efficiency and economic feasibility of microalgae biotechnology could be increased by reducing the expenses associated with harvesting and extracting bioproducts through the development of automated cultivation systems, which is made possible by this breakthrough [7,18]. AI technology can also speed up the optimisation process, offer useful predictive analytics, and assist in revealing system dynamics and uncertainties in the context of microalgae-based biorefineries [2,7]. A computer programme is introduced using machine learning (ML), which is a subset of AI. These programmes use a large amount of data that are led by certain statistics and algorithms. ML facilitates data tracking. ML creates predictive models and heuristics to be used later. Data mining is a type of ML approach that analyses a vast amount of data to generate certain patterns based on historical data and predict the real future [23]. Researchers revealed insightful information about how ML might be applied to microalgae research, especially to improve techniques for cultivation. They noted that the intricacy of development dynamics presents challenges for conventional microalgal production techniques [24,25]. Traditional methods, which are impacted by factors including temperature, light intensity, pH, and nutrient availability, frequently lack real-time monitoring, which makes it difficult to respond promptly to changes that affect microalgal growth. These restrictions highlight ML’s enormous potential for expanding the study of microalgae [24]. Similarly, a network of detecting and actuating devices known as the Internet of Things (IoT) enables information sharing via a single platform. Without the need for human-to-human or human-to-computer interaction, these “things” or devices can send an enormous amount of data over a network [26].
The most recent AI technologies used to enhance microalgal culture are compiled in this review. This review is impactful and focused on the AI applications for the improvement of the microalgae industry. It provides a thorough evaluation of the applications of different AI technologies to the advancement of microalgal cultivation. AI integration with microalgal cultivation is essential to the rapidly expanding microalgae industry. As a result, this work sheds light on the implications of using AI approaches to increase microalgal productivity for commercial purposes. Additionally, it offers a thorough understanding of various AI models and their applications in various aspects of microalgal cultivation, including improved lipid production, automated monitoring, cross-species comparison, genome editing enabled by bioinformatics, and optimisation of various physical parameters related to microalgal cultivation.

2. Methodology

The most recent AI applications used in microalgae cultivation were prioritised during the preparation of this review article. However, knowledge of various AI models and terms associated with AI is also necessary to comprehend its application. Therefore, this review also covers these to some extent. The last 20 years’ worth of literature was gathered for this review piece, with the latest ten years’ worth of literature being the primary focus.

3. AI Models Used in Microalgae Cultivation

As AI’s algorithms can successfully handle the complexity of unpredictable biosystems, its use in microalgae research is growing in popularity. The production of microalgae could be greatly increased using AI. Researchers can more accurately identify, categorise, and monitor different algal strains and their growth patterns by using AI to optimise production conditions. This breakthrough paves the way for the creation of automated growing systems, which may reduce the expenses associated with collecting and processing bioproducts, increasing the effectiveness and financial sustainability of microalgae biotechnology [7]. Some of the most popular AI models used in microalgae cultivation are summarised below:

3.1. Artificial Neural Network (ANN)

ANN is a computer model that draws inspiration from the central nervous systems of humans and animals. This system is made up of a network of interconnected “neurones” that can compute values from inputs [27]. In a study by [28], six genera of microalgae were classified based on ANN.

3.2. Genetic Algorithm (GA)

A GA is a random search and optimisation method inspired by the principles of natural genetics. Natural selection and genetics serve as the foundation for GA, an adaptive heuristic search algorithm [29]. In a study by [30], GA was used for the culture media optimisation of Nannochloropsis gaditana, which resulted in the enhancement of eicosapentaenoic acid yield by 23%.

3.3. Deep Learning (DL)

DL is a subfield of ML techniques that involves learning several levels of abstraction and representation. It may handle data in its unprocessed form and automatically identify the representations required for detection or classification [31]. Computer vision-based DL models are essential for detecting and categorising dangerous algal blooms in aquatic habitats and water storage systems. In the field of image recognition and related applications, such as the classification and identification of microscopic algae species, DL techniques have demonstrated a noteworthy and impressive performance [32]. A Convolutional Neural Network (CNN) is a type of deep neural network including interconnected components. One of the most studied DL methods, CNNs execute convolution operations on unprocessed data and are widely used in image classification [33]. In a study by [34], CNN was used for the classification of microalgae.

3.4. Decision Tree (DT)

A DT is a supervised learning technique that can be used for tasks involving both regression and classification. It operates by making a series of choices that direct the model towards a specific result. DT efficiently classifies data into several classes in classification issues by dividing the data into discrete groups according to the response variable [7].

3.5. Support Vector Machine (SVM)

For supervised learning, an SVM is a reliable and adaptable method. It can be applied to a variety of tasks, including regression, forecasting, pattern recognition, and both linear and nonlinear classification [7].
In a recent study, an integrated transformer-based framework for automated localisation and recognition of small, medium, and large algae species was implemented to increase the detection accuracy of multiple algal species in real, complex backgrounds in a study by [35], which collected multi-species algae samples from real water environments.
Table 1 provides an overview of how various AI models are used by various species with varying parameters, input, output, and efficiency.
Table 1. A summary of neural and non-neural network-based techniques for forecasting the growth and biomass of microalgae.
Table 1. A summary of neural and non-neural network-based techniques for forecasting the growth and biomass of microalgae.
SpeciesModelInputOutputEfficiencyReferences
Kleibsormidium sp., Dictyosphaerium sp., Desmodesmus sp., Scenedesmus sp., and Micractinium sp.ANNAverage solar irradiation, average water temperature, average pH, initial microalgae concentration, harvesting time, hydraulic retention time, addition of sodium acetate, and nitrate concentrationConcentration of microalgae throughout the cultivation phaseCoefficient of determination (R2) = 0.93[36]
Chlamydomonas reinhardtiiGA, ANNFluorescence emission spectraConcentration of cellR2 = 0.998 Mean square error (MSE) = 0.0000998[37]
Chlorella vulgarisANNInitial biomass, phosphate, glucose, and nitrate concentrations; yield coefficientsVariation in the concentrations of biomass, phosphate, glucose, and nitrateNot mentioned[38]
Spirulina platensisMulti-Layer perceptron (MLP)Temperature, light intensity, pH, dissolved oxygen, rate of oxygen production, harvesting duration, nitrate, phosphate, bicarbonate, and initial biomassOptical density, trichome size, and trichome concentrationR2 > 0.94[39]
C. vulgarisResponse surface methodology (RSM) and PLPCultivation time and pHConcentrations of biomass, total fat, unsaturated fat, and oleic acidR2 = 0.92 Root mean square error (RMSE) = 65.11[40]

4. AI’s Applications in Microalgae Cultivation

Recent improvements in microalgae technology have focused on minimising production costs, as the existing expenses related to cultivation and harvesting impede sufficient profit margins [7,18]. To address these issues, researchers initially employed conventional mathematical modelling and simulation techniques; however, the current approach involves the integration of AI to find solutions to all challenges [7,41]. Figure 1 illustrates the various AI algorithms’ applications in microalgal research [2].
Digitalising microalgae cultivation and harvesting can substantially reduce operational costs [7,11]. AI technology can boost photobioreactor (PBR) performance by ensuring consistent and optimal biomass output. This goal is achieved by the use of interconnected sensors that monitor microalgae development, allowing for modifications in circumstances as needed. The optimal growth parameters can be determined with the aid of ML methods [7,22]. The integration of AI and IoT into microalgae operations has enormous potential to advance sustainability in three important domains: social, economic, and environmental [7,42]. AI is also crucial for real-time monitoring and automation, replacing laborious and destructive sampling with noninvasive image analysis and sensor fusion that assess cell density, morphology, and pigment accumulation [7,43]. Soft sensors enhanced by AI estimate critical variables such as intracellular nutrients and dissolved CO2, supporting automated and efficient control [44,45,46].
Computer vision pipelines can classify species, count cells, and estimate intracellular pigments in near real time. In the microalgal species Haematococcus, image-analysis systems have quantified morphological transitions during astaxanthin induction and extracted dozens of single-cell features from bright-field and colour micrographs, enabling early detection of stress [47]. More recently, classification models have been trained to recognise cell-cycle stages of Haematococcus lacustris online, which is directly useful for timing stress induction to maximise astaxanthin per cell [48]. Beyond RGB (red, green, blue) images, hyperspectral imaging paired with ML can noninvasively estimate pigment content such as C-phycocyanin in Spirulina or astaxanthin in Haematococcus, opening the door to closed-loop pigment production without frequent High-Performance Liquid Chromatography (HPLC) assays [49,50]. These tools raise product consistency and reduce downtime by catching deviations early. Purely mechanistic models based on mass transfer, light attenuation, and kinetics often struggle with scale-dependent effects and unmodelled disturbances [51,52].
The benefits of both data-driven and physics-based models are combined in a hybrid model. A hybrid model uses prior information to either design the model’s structure or limit the parameters of a data-driven model. The variables in a hybrid model have specific physical meanings due to the limitation of prior information. Compared to data-driven models, a hybrid model constructed from reduced mathematical equations for the physical process would exhibit more reliability with limited extrapolation and require less data for model training [53]. Hybrid modelling couples first-principles structure with data-driven error correction, improving accuracy without discarding physics. A study by [51] demonstrated the reduced-order hybrid models tailored to microalgae cultivation, which are fast enough for real-time optimisation yet accurate across operating ranges. At the system level, an algal digital twin (ADT) links a live reactor to a virtual replica fed by multi-sensor data and updated by learning algorithms. This setup supports soft sensors, anomaly detection, and scenario testing before pushing new setpoints to the real plant. A new cutting-edge technology called a “digital twin” integrates different kinds of three-dimensional (3D) model data, including real 3D scenes, 3D terrains, and 3D entities, to precisely represent physical items in the virtual world [54]. An ADT holds great potential for transforming existing microalgal cultivation for the production of biomass, such as raceway ponds, into sustainable algal management systems (nitrogen, phosphorus, turbidity, temperature, dissolved oxygen, carbon dioxide, chlorophyll-a, pH, etc.) and developing their infrastructure to make them more economical and energy-efficient for the cultivation of algal biomass [51].
Furthermore, AI bridges the scalability gap through hybrid models that combine physics-based and data-driven approaches, improving both accuracy and computational efficiency. These models underpin ADT that simulate and optimise reactor performance under fluctuating conditions, making AI indispensable for modern, sustainable microalgae cultivation [51,55]. AI has an ability to completely transform the production of microalgae by facilitating predictive decision-making, process optimisation, and intelligent data analysis. Large datasets collected from microalgae cultivation systems can be analysed by machine learning algorithms to detect trends, estimate growth rates, and optimise environmental conditions [56,57]. AI-based models can simulate various growth conditions to ascertain the optimal temperature, light intensity, and nutrient composition needed for the growth of bioactive compounds with the highest possible output [57]. Figure 2 depicts a framework for integrated AI-enabled smart systems for microalgal culture and resource recovery [58].
Microalgae are a diverse group of photosynthetic microorganisms with immense biotechnological potential, from renewable biofuel production to CO2 capture and high-value bioproducts. They exhibit rapid growth and high biomass yields without competing for arable land or freshwater, making them attractive for sustainable production systems [59]. Optimising microalgal cultivation is challenging due to complex interactions of factors such as light, temperature, pH, nutrients, and species-specific biology. Traditionally, finding optimal conditions relied on trial-and-error or simple empirical models, which is time-consuming and often suboptimal. In the past decade, AI techniques have been increasingly applied to address these challenges. Advanced ML algorithms, including SVMs, DTs, random forests (RF), and ANNs, as well as DL models (deep neural networks, CNNs) and computer vision systems, have been leveraged to improve production efficiency, yield, and process control in microalgal cultivation. Each AI technique offers unique strengths, like ANNs, which excel at capturing complex nonlinear relationships, while evolutionary algorithms like GAs can optimise multivariate conditions [7]. Figure 3 illustrates a typical workflow for an ML model [58].
Similarly, Figure 4 illustrates a flowchart of an AI/IoT-assisted microalgae cultivation system [42].

4.1. AI Techniques for Optimising Biomass Production

To maximise the output and effectiveness of microalgae systems, conditions for growth must be optimised. Light intensity, temperature, pH, nutrient concentration, and carbon dioxide availability are only some of the environmental factors that microalgae are susceptible to. Their growth rates, biomass yield, and the general quality of the biomass generated are all greatly influenced by the interaction of these variables (Table 2) [15].
Table 2. AI methods for microalgae cultivation optimisation [15].
Table 2. AI methods for microalgae cultivation optimisation [15].
AI TechnologyOverviewApplications
MLAlgorithms that improve their performance through repeated trainingPredictions for the optimisation of growth conditions
Genetic algorithmsAlgorithms for optimisation motivated by natural selectionImprovement for strain for enhanced productivity
Data MiningDeriving valuable information from extensive datasetsRecognising trends in productivity and growth
Neural NetworksComputer models that simulate how the human brain worksExamining complicated relationships among variables
AI-driven modelling has proven especially effective at identifying optimal growth conditions from large datasets, outperforming conventional models. The ANN growth model for the cyanobacterium Synechocystis sp. PCC 6803 achieved a validation R2 value of 0.97, making it approximately 76% more accurate than a traditional light–dark (mechanistic) model in simulating growth under different light regimes [60]. Long short-term memory (LSTM) deep learning networks have been used to incorporate time-series data (e.g., fluctuating outdoor light) for predicting biomass in outdoor cultures. For instance, an LSTM-based model for the marine diatom Phaeodactylum tricornutum cultivated outdoors could account for light acclimation history and outperform simpler models in forecasting growth, enabling optimised harvest timing and biomass sensing strategies [61,62]. ML algorithms have also been applied to boost biomass output, such as SVR models combined with Bayesian optimisation, which have optimised cultivation parameters for greens like Chlorella and Scenedesmus. In a study, an SVR (enhanced by Bayesian hyperparameter tuning) was used to maximise C. vulgaris biomass productivity and CO2 fixation; it achieved a high R2 of 0.911 in validation. The model suggested optimal conditions (e.g., 40 °C, a 1:1 nitrogen-to-phosphorus ratio, and a 12:12 h light–dark cycle) that yielded biomass productivity of 0.098 g/L/day and CO2 uptake of ~0.141 g/L/day [63,64,65].
AI has also enabled real-time control and automation of biomass production. Modern PBRs increasingly integrate IoT sensors and AI controllers to adjust conditions on the fly. Ref. [66] developed an IoT-linked system for Arthrospira platensis (Spirulina) that monitors temperature, light, turbidity, and gas flow in real time. While their initial system maintained stable conditions manually, subsequent integration of ML can allow autonomous, predictive adjustments (e.g., preventing temperature overshoot or nutrient depletion). Another study designed a closed tubular photobioreactor with continuous imaging and sensor feedback, coupled to ML models for growth forecasting. By using an XGBoost ensemble (gradient-boosted trees) on streaming data, they achieved extraordinarily high accuracy in predicting biomass and were able to optimise the light regime for maximum growth [67]. A study by [68] reported that a hybrid algorithm, convolutional neural network-genetic algorithm (CNN-GA), was applied to optimise input parameters to maximise phycobiliprotein (PBP) production and cell growth in Nostoc sp. CCC-403. The model focused on three BG-11 media components (Ferric ammonium citrate, K2HPO4, and MgSO4) and the pH as input factors. The CNN-GA predicted the optimal conditions, resulting in a 90% increase in biomass yield and a 61.76% enhancement in PBP recovery. This study demonstrated the effectiveness of the CNN-GA approach in optimising cultivation parameters for improved biological production.

4.2. Enhancing Lipid Accumulation for Biofuels

Many microalgal species can accumulate substantial lipids (oils) under certain conditions, making them promising biofuel feedstocks. AI techniques have been at the forefront of optimising lipid productivity, which often involves inducing cellular stress (like nitrogen deprivation) to trigger oil storage without excessively compromising growth. Researchers have applied both data-driven modelling and computational optimisation to navigate this trade-off. For example, a study used both response surface methodology (RSM) and an ANN to predict maximum lipid content in Chlorella minutissima under various treatments. The ANN (trained on inputs like wastewater concentration and enzyme additives) achieved an R2 of 0.963, outperforming the RSM model in prediction accuracy [69]. AI has also been used to link stress physiology with lipid outcomes. A study demonstrated a hybrid approach by combining gene expression meta-analysis with supervised ML to investigate salinity-induced lipid accumulation in Dunaliella species [70]. Their model pinpointed key meta-genes involved in lipid metabolism and reactive oxygen stress responses, revealing interactions (such as Ca2+ signalling crosstalk with lipid pathways) that were conserved across two Dunaliella strains. These insights, derived from ML on transcriptomic data, suggest genetic engineering targets for improving lipid production. Although this example goes beyond traditional process parameters, it tells how AI can handle high-dimensional biological data to uncover factors that ultimately influence lipid yields.
On the process optimisation side, deep neural networks and hybrid models have shown particular success in maximising algal oil output. A recent study used a deep neural network in tandem with RSM to optimise the growth temperature for Chlorella sp. aimed at CO2 capture and lipid production. The DNN predicted an optimal temperature of 29.55 °C intriguingly, slightly higher than the 28.7 °C optimum predicted by RSM, and achieved 95% prediction accuracy for biomass and lipid metrics. Implementing the DNN’s recommended temperature led to a measured 15% improvement in biomass productivity compared to traditional fixed-temperature control, with corresponding gains in lipid accumulation [15,71]. This demonstrates how deep learning can refine single-factor optima beyond what classical methods suggest. A neuro-fuzzy model (ANFIS) that was enhanced by a GA was applied to a compiled dataset of various algal strains to predict CO2 fixation rates, which correlate with biomass and lipid generation under different pH, CO2, and nutrient conditions. The GA-tuned ANFIS achieved an R2 value of 0.9846, markedly better than the standard ANFIS without GA, which indicates that evolutionary optimisation of model parameters can greatly improve predictive power for complex outcomes like carbon fixation and lipid synthesis [72]. Overall, through such approaches, AI-driven models have helped boost lipid yield and oil quality.

4.3. Optimising CO2 Sequestration and Carbon Capture

CO2 uptake by microalgae depends on factors like the concentration of CO2 supplied, gas transfer rates, illumination, and nutrient status. AI algorithms have been used to both predict CO2 fixation rates under various conditions and to control cultivation parameters to maximise carbon capture efficiency. For instance, research by [63] optimised C. vulgaris cultivation for CO2 biofixation using multiple AI models (ANN, boosted regression trees, SVR) combined with Bayesian optimisation. The best model (SVR) yielded an R2 of 0.911 and negligible bias in predicting CO2 uptake. In practical cultivation systems, precise CO2 regulation greatly enhances growth efficiency. A 2016 study using a microcontroller-based CO2 injection system with MG-811 sensors maintained levels above 350 ppm, yielding 16.5% more biomass than passive aeration. Modern AI systems expand this idea by using supervised and reinforcement learning to dynamically adjust CO2 flow and predict optimal aeration strategies. Advanced models, such as ANN–GA frameworks, have even optimised real flue gas utilisation, achieving high CO2 sequestration rates while simultaneously treating wastewater [73,74].

4.4. Computer Vision and Automated Monitoring

A significant branch of AI applications in microalgal cultivation focuses on computer vision and image analysis, facilitating non-invasive monitoring of culture dynamics and enabling accurate identification of microalgal species and physiological status. A notable example is the use of hyperspectral imaging for real-time monitoring. A study developed a transmission hyperspectral microscopic imager combined with ML analysis to optimise cultivation of a marine alga (Phaeocystis sp.). This system captured spectral images of microalgae with high spatial (4 µm) and spectral (3 nm) resolution and then applied techniques like principal component analysis and SVM classification to identify microalgae species in a mixed sample with 94.4% accuracy [75]. Future developments are expected to employ deep convolutional neural networks for analysing cellular morphology and biochemical traits, enhancing automation and precision in PBR management.

4.5. Cross-Species Comparisons of AI Applications in Microalgal Cultivation

AI models have been increasingly applied across diverse microalgal species to optimise biomass production, lipid accumulation, and CO2 fixation. However, species-specific physiological differences often limit model generalisation, emphasising the need for adaptable, data-driven, or hybrid approaches [7,15,76]. In a study by [70], the salt stress responses of Dunaliella salina and Dunaliella tertiolecta were investigated by combining supervised ML with RNA-seq meta-analysis, which found core meta-genes associated with autophagy, photosynthetic apparatus proteins, lipid and nitrogen metabolism, and ROS-related pathways using Fisher’s p-value combination for cross-species analysis. Important connections between ROS networks, lipid buildup, and Ca2+ signalling under salt stress were also identified. This integrative method enhances strain development for optimal metabolite synthesis and identifies potential genes for genetic engineering. Conventional approaches frequently depend on trial-and-error, which can be laborious and imprecise. AI models, on the other hand, offer a more methodical and data-driven strategy to find important metabolic pathways and potential genes for genetic manipulation.
Below is the table that shows emerging strategies such as hybrid modelling, aggregated datasets, and explainable AI to improve generalisation and interpretability across species (Table 3) [7,15,76].
Table 3. Comparisons of AI applications in microalgal cultivation across species.
Table 3. Comparisons of AI applications in microalgal cultivation across species.
AspectObservation and ExampleAI Technique UsedSpecies InvolvedKey Insight and OutcomeReferences
Species-specific model performanceDue to physiological differences, AI models developed for one species frequently perform poorly when applied to another (light response, food uptake, and stress tolerance)ANNSynechocystis vs. ChlorellaEach strain requires correction in order to retain accuracy[15]
Aggregated modellingMerged datasets from several studies to produce forecasts that are broadly applicableDecision treeMixed species (>100 studies)Revealed broad trends in biomass and lipid optimisation[15,77]
Multi-species CO2 fixation modellingPredicts CO2 fixation across different algal speciesAdaptive neuro-fuzzy inference system optimised by genetic algorithm (ANFIS–GA)Multiple algaeIncreased capacity for prediction, but limited by the variability of the data[15]
Hybrid modelling approachCombines mechanistic and data-driven models for adaptabilityHybrid ML–mechanisticGeneral applicationImproved generalisation and interpretability[76]
Interpretability in harvesting optimisationExplains influence of species traits (cell size, morphology) on harvestingExtreme gradient boosting + shapley additive explanations (SHAP)Various microalgaeAchieved (R2 = 0.93); highlighted species-specific harvest efficiencies[15]
Nutrient optimisation differencesOptimal N:P ratio differs even among close speciesANN/RegressionChlorella kessleri vs. C. vulgarisDemonstrated distinct nutrient needs despite phylogenetic similarity[15,76]
Data scarcity solutionsLimited datasets hinder AI generalisation to new species.Synthetic data generation/Transfer learningRare or new strainsEncourages dataset sharing and transfer learning for rapid adaptation[7,76]

4.6. AI-Based Bioinformatics for Genome Editing

The current status of AI algorithms has made it possible for experimental conversion to yield optimal conditions under uncertainty and predictions with high accuracy. Performance prediction and optimal condition selection are the two most popular uses of AI in experimental conversion (Table 4) [2].
Table 4. AI algorithm applications in the conversion of microalgae.
Table 4. AI algorithm applications in the conversion of microalgae.
SpeciesConversion TechnologyAI AlgorithmApplication OutcomeReferences
Algal MatPyrolysisSingle-layer ANNPredicted pyrolysis behaviour and improved understanding of thermal degradation characteristics[77]
C. vulgaris, N. oceanica, Chlamydomonas sp.PyrolysisParticle Swarm Optimisation (PSO) combined with independent parallel reaction modelModelled microalgal pyrolysis kinetics by considering carbohydrates, proteins, and lipids as input parameters[78]
C. vulgarisPyrolysis and GasificationNeuro-evolution integrated with deep neural networksPredicted thermal conversion efficiency and identified optimal operating conditions to minimise energy use[2]
Spirulina sp.CombustionSingle-layer ANN coupled with numerical methodsPredicted combustion efficiency, exhaust emissions, and blend performance for algal biodiesel formulations[79]
Nannochloropsis oculataHydrothermal liquefaction (HTL)Multiple linear component additivity modelSimulated HTL conversion behaviour for yield and bio-crude quality optimisation[80]
Chlorella CG12Transesterification (supercritical methanol)RSM, ANN, and GAOptimised reaction conditions for biodiesel production under supercritical methanol[81]
Jatropha–AlgaeTransesterification (KOH-catalysed)Neuro-Fuzzy inference system (NFIS) integrated with RSMPredicted transesterification outcomes considering catalyst concentration, temperature, and reaction time[82]
Chlorella sp.Ultrasonic-Assisted transesterificationSingle-layer ANN integrated with RSMModelled ultrasonic power, methanol ratio, and reaction time to enhance FAME content and exergy efficiency[83]
Mixed Microalgal BiomassEnzymatic hydrolysisSingle-layer ANNPredicted sugar yield by correlating substrate concentration, temperature, pH, and retention time[84]
The process of genetic modification using techniques like ribonucleic acid interference (RNAi), zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) (CRISPR-Cas9) can be aided by AI algorithms to maximise the yield and selectivity of the microalgae nutrition component as biomolecules. A technique for increasing lipid accumulation is the suppression of lipid catabolism; for example, the RNAi knockdown approach increased the generation of photosynthetic H2 from Chlamydomonas reinhardtii [2,85]. TALENs, including nucleases, were utilised for targeted genome editing on Phaeodactylum tricornutum for the nutrients, and ZFN-mediated gene editing was employed in Chlamydomonas reinhardtii to increase lipid production [2,86,87]. Furthermore, CRISPR-Cas9 was described as a quick and efficient method for stable gene editing in the microalgae P. tricornutum [2,88]. Table 5 lists additional uses of genome editing applications on microalgae [2].
Table 5. AI-driven microalgae genome editing in microalgae.
Table 5. AI-driven microalgae genome editing in microalgae.
Microalgal SpeciesGenetic ToolScientific Function/ApplicationReferences
Dunaliella salinaRNAiRNAi was employed to generate gene knockouts and clone sequences, enabling regulation of specific metabolites and modulation of host cell physiology[89]
Chlamydomonas reinhardtiiRNAiUsed to silence chlorophyllide and oxygenase genes, facilitating the functional characterisation of gene deactivation and its physiological consequences[90]
C. reinhardtiiZFNsZFNs were applied to target the COP3 gene, leading to altered phenotypic and physiological expression patterns[91]
Nannochloropsis oceanica IMET1ZFNsZFN-mediated transformation enabled chloroplast mutagenesis to regulate uric acid biosynthesis and improve chloroplast engineering efficiency[92]
Phaeodactylum tricornutumTALENsTALENs introduced targeted double-strand breaks at the PtAurea gene, a blue-light photoreceptor, allowing precise control over light response and colony formation[93]
C. reinhardtiiTALENsTALEN-based activation of ARS1 and ARS2 loci enhanced nutrient compound accumulation and promoted targeted genetic modifications in host cells[94]
C. reinhardtii CC-124CRISPR–Cas9CRISPR–Cas9 enabled efficient, site-specific mutagenesis with greater precision and consistency than RNAi, improving strain stability[95]
Nannochloropsis oceanica CCMP1779CRISPR–Cas9Utilised for high-lipid metabolism studies, the CRISPR–Cas9 system incorporating ribozyme-linked sgRNA enabled autonomous, targeted mutagenesis for lipid pathway optimisation[96]

4.7. Optimising Light, Temperature, Nutrients, Harvesting, and Extraction

IoT involves the interlinking of sensors, devices, and control systems that gather and transmit real-time data through the internet. In microalgae cultivation, IoT-based systems can provide continuous monitoring and control of key environmental parameters, including light intensity, temperature, pH, dissolved oxygen, and nutrient levels. In microalgae cultivation, IoT-based systems provide continuous monitoring and regulation of critical environmental parameters, such as light intensity, temperature, pH, dissolved oxygen, and nutrient concentrations [57]. Table 6 presents the comparison of recently used AI technologies in the optimisation and advancement of microalgal cultivation.
Table 6. Table of comparison showing the recent applications of AI for the optimisation of microalgal cultivation.
Table 6. Table of comparison showing the recent applications of AI for the optimisation of microalgal cultivation.
Optimisation TechnologyDescriptionBenefitsReferences
LightArtificial neural network (ANN)To optimise light conditions for Parachlorella kessleri’s production of polyphenols, ANN was combined with a genetic algorithm (ANN-GA)Greater efficiency in computation; Time saving; exhibiting strong performance in a variety of light levels and photoperiods[15,97]
LightMLA closed tubular photobioreactor was constructed with sensors to track temperature, light intensity, and other variables. ML models were then integrated to predict growth dynamicsImprovement of biomass productivity; Greater precision in predicting growth[15,67]
TemperatureDeep neural network (DNN) and response surface methodology (RSM)Temperature optimisation on C. vulgaris cultivation for carbon dioxide capture was performed using DNN and RSMIncreased biomass productivity and CO2 capture efficiency[15,71]
NutrientsSupport vector regression (SVR) and GAUtilising SVR together with GA to optimise Chlorella kessleri’s nitrogen–phosphorus ratio in municipal wastewater treatmentImproved nutrient removal efficiency[15]
Light and temperatureIoTDevelopment of an IoT-based system to maximise Arthrospira cultivation using sensors and Arduino microcontrollers for real-time monitoring of important factors like turbidity, light intensity, and water temperatureMaintenance of stable water temperature; Regulation of light intensity; Optimisation of turbidity level; Balance of nitrogen, oxygen, and CO2 supply[15,66]
Harvesting and extractionIoTDevelopment of an IoT-based system to optimise the growth and harvesting of Spirulina, employing real-time sensors to track important variables like water temperature, UV light intensity, and turbidityImproved harvesting procedures; Increased production efficiency; Generated useful data for larger-scale applications and additional research[15]

5. Sustainability of AI/IoT in Microalgae Cultivation

Numerous hardware components are deployed during the IoT implementation process. The cost of maintenance is therefore a significant economic concern. It has been reported that the adoption of AI/IoT across industries reduces maintenance costs by 12–40% [42,98], and a similar trend is expected to apply to microalgae processes, contributing to a reduction in production costs. Furthermore, according to reports, 82% of hardware failures are random, with the remaining 18% occurring as a result of ageing. Frequent hardware maintenance is therefore crucial to preventing hardware malfunctions or breakdowns that could impair process productivity as a whole [42]. By optimising the ideal culture conditions with the least amount of chemical usage and generating high microalgae biomass production, the installation of IoT sensors and the application of the machine learning optimisation approach have the potential to increase resource use efficiency [42]. Microalgae output projections may be predicted using an ML predictive model, which will help the company improve its business and production strategy. These will lessen the overall environmental impact of microalgae cultivation by reducing chemical waste [42]. Even though the adoption of IoT and AI would have resulted in lower labour costs, it might still open up new job prospects for society [42]. Moreover, a recent study demonstrated the usefulness and effectiveness of ultraviolet (UV)-visible spectroscopy in conjunction with ML algorithms for the identification and description of biological contaminants in microalgae cultivation [99].

6. Limitations and Possible Remedies for Microalgae Cultivation Using AI Models

The quantity and quality of datasets are crucial for data-driven models like ML algorithms, which are frequently constrained by laborious data collection procedures. Furthermore, systematic disturbances and large measurement errors are common in industrial datasets [100]. Furthermore, state variable values at predetermined time intervals are necessary for the majority of data-driven models. However, depending on the effectiveness and accessibility of the analytical equipment, data is often gathered at different periods at a plant. Additional difficulties with this inconsistent dataset include missing data for the creation of data-driven models [55]. In order to reduce the limitations of physics-based and data-driven models and encourage their application to industrial bioprocesses, a hybrid semi-parametric modelling framework that utilises the benefits of both approaches has been proposed in the literature. This framework can be readily integrated into a variety of online optimisation techniques. Additionally, the hybrid semi-parametric modelling approach is thought to be a useful strategy for identifying potential issues associated with industrial biosystem optimisation, such as the low quantity and quality of datasets, the lack of physical knowledge of the process, online measurements, and the high cost of periodic sampling [76].

7. Conclusions and Future Perspectives

The underlying physics of algae growth processes is not inherently understood by ML models; nonetheless, mechanistic or kinetic models are superior in this regard, but their simple representations frequently result in poor forecast accuracy. A promising answer is provided by a hybrid learning strategy that combines the advantages of physics-based models and machine learning. By combining these methods, the drawbacks of each can be lessened, resulting in more precise forecasts, better extrapolation capabilities, increased interpretability, and a decrease in the amount of data needed for modelling [76]. AI has emerged as a key component of microalgal culture research in the last ten years, resulting in significant advancements in production and growth. By optimising variables like light, temperature, and nutrients, machine learning and deep learning models have increased biomass and important metabolite yields. The effectiveness of CO2 sequestration has also been improved by AI-based control systems, bolstering the contribution of microalgae to climate solutions. Numerous species, including Chlorella, Arthrospira, Nostoc, and Dunaliella, have benefited from the effective application of techniques including neural networks, evolutionary hybrids, support vector machines, and computer vision. As AI and microalgae technology continue to develop together, a number of important research avenues show promise for improving the effectiveness and usefulness of this integration. Scaling up still presents difficulties, though, such as a lack of high-quality datasets, complicated model interpretation, and interaction with reactor systems operating in real time. These problems are being addressed by initiatives like transfer learning, adaptable AI frameworks, and the use of simulation data. All things considered, additional breakthroughs in AI could boost the effectiveness, sustainability, and commercial viability of microalgal biotechnology in both real-world and pilot-scale industries. Furthermore, phenotyping, autonomous cultivation, and AI-based contaminant prediction are other essential areas for advancement in the future.

Author Contributions

Conceptualization, V.R.; methodology, V.R., M.H. and S.J.; software, V.R. and M.H.; validation, V.R., M.H., H.S. and S.J.; formal analysis, V.R. and S.J.; investigation, V.R. and M.H.; resources, V.R., M.H., H.S. and S.J.; data curation, V.R. and M.H.; writing—original draft preparation, V.R. and M.H.; writing—review and editing, V.R., M.H., H.S. and S.J.; visualisation, V.R., H.S. and S.J.; supervision, S.J. and H.S.; project administration, S.J. and H.S.; funding acquisition, S.J. and H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the “Regional Innovation Mega Project” programme through the Korea Innovation Foundation, Ministry of Science and ICT (2023-DD-UP-007), and by the Basic Science Research Program (NRF-2021R1A6A1A03039503). It was also supported by the Korea Institute of Marine Science & Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries (Development of mass production process standardization of xanthophyll astaxanthin, 20220379) and Techniques Development for Management and Evaluation of Biofouling on Ship Hulls (20210651). It was also supported by the Soonchunhyang University research fund.

Data Availability Statement

All data are within this manuscript.

Conflicts of Interest

Author HyunWoung Shin was employed by the AlgaeBio, Inc. The remaining authors affirm that there were no financial or commercial ties that might be interpreted as a potential conflict of interest during the research.

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Figure 1. AI algorithm applications in microalgae research.
Figure 1. AI algorithm applications in microalgae research.
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Figure 2. Framework for integrated AI-enabled smart systems for resource recovery and microalgal cultivation.
Figure 2. Framework for integrated AI-enabled smart systems for resource recovery and microalgal cultivation.
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Figure 3. An ML model’s typical workflow.
Figure 3. An ML model’s typical workflow.
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Figure 4. A systematic flowchart of an AI/IoT-assisted microalgae cultivation system.
Figure 4. A systematic flowchart of an AI/IoT-assisted microalgae cultivation system.
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Rayamajhi, V.; Hussain, M.; Shin, H.; Jung, S. Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation. Processes 2025, 13, 3764. https://doi.org/10.3390/pr13123764

AMA Style

Rayamajhi V, Hussain M, Shin H, Jung S. Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation. Processes. 2025; 13(12):3764. https://doi.org/10.3390/pr13123764

Chicago/Turabian Style

Rayamajhi, Vijay, Mudasir Hussain, Hyunwoung Shin, and Sangmok Jung. 2025. "Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation" Processes 13, no. 12: 3764. https://doi.org/10.3390/pr13123764

APA Style

Rayamajhi, V., Hussain, M., Shin, H., & Jung, S. (2025). Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation. Processes, 13(12), 3764. https://doi.org/10.3390/pr13123764

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