Artificial intelligence in microbial biotechnology for food security: current state and challenges

1 Introduction

The world food system faces unprecedented challenges from climate-driven agricultural instability to a rapidly growing population that is projected to reach between 9.6 and 9.8 billion by 2050 (United Nations, 2024; Munje and Moharil, 2026). Climate-driven agricultural instability is often due to unpredictable weather patterns, including prolonged droughts and intense floods, which severely impact crop production and food availability. These events often negatively affect the intricate microbial ecosystems within agricultural soils that play central roles in nutrient cycling and plant health. These systems are also strongly sensitive to these environmental disruptions, potentially exacerbating food insecurity via a marked reduction in soil fertility and crop resilience (Malkawi and Kapiel, 2024).

In sub-Saharan Africa, these challenges are pronounced; for instance, cassava serves as a staple food in this region and has potential for industrial biofuel applications yet yields typically range from merely eight to ten tonnes per hectare (t/ha), markedly lower than the 20–22 t/ha observed in Asia, attributable to biotic constraints such as cassava bacterial blight (Xanthomonas axonopodis pv. manihotis) (Fanou et al., 2018; Sakadzo et al., 2025).

Concurrently, the extensive pesticide and chemical fertilizer usage further negatively impact soil microbial diversity, offsetting the established natural balance of biological feedback control mechanisms and nutrient assimilation pathways (Tripathi et al., 2020). This pervasive degradation of the soil and its associated ecosystems is a constant nudge for novel biotechnological interventions that leverage natural, biological solutions for long-lasting and sustainable agricultural practices, capable of lessening these detrimental impacts with more promising assurance of long-term food security. For example, cassava bacterial blight can lead to nearly 100% yield loss, significantly impacting peasant farmers who have limited or no access to modern agricultural resources such as improved seeds and eco-friendly pesticides. Other viral blights in this region, such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), may amount to losses of between $1 and $1.5 billion annually (Kwame Torkpo and Amponsah, 2024). Such substantial losses are key pointers to the critical need for advanced biotechnological interventions that enhance crop resilience, overall agricultural productivity, and food security.

Microbial biotechnology can offer a sustainable solution by harnessing the metabolic pathways of microorganisms to improve crop resilience and soil health (Aghdam et al., 2024). An example is the significant potential of microbial-based biotechnology in agriculture, including rhizospheric microbiome-driven biofertilizers and effective probiotics that enhance crop yield (Priyadarshini et al., 2024). A more powerful framework is the integration of artificial intelligence (AI) into microbial biotechnology, which has initiated a fundamental shift, revolutionizing its applications across all fields of biotechnology, including healthcare (Oyeniran et al., 2025; Siddique et al., 2025; Yu et al., 2025).

However, the overwhelming complexity of biological data from high-dimensional omics to heterogeneous environmental variables now, more than ever, requires AI integration to navigate and harness its benefits for maximum application (Kim, 2019; Pathak et al., 2025). Indeed, AI in biological data applications, such as predictive modeling and precision agriculture, may offer useful capabilities in mitigating the impacts of climate change on crop yields (Zidan and Febriyanti, 2024). Moreover, AI can identify novel microbial solutions for climate change adaptation by exploring the vast metagenomes of uncharacterized microorganisms and their functions, thereby providing a timeous output, especially in scenarios of time-critical research (Gilbert and Zengler, 2025). Despite this progress, challenges like data availability, interpretability, policy and bureaucratic bottlenecks still exist (Figure 1).

Despite its attendant challenges, progress achieved so far reassures of a step in the right direction for the future of microbial biotechnology.

This review explores how artificial intelligence is transforming microbial biotechnology to enhance global food security, specifically focusing on its current applications, significant challenges, and prospects.

2 AI architectures in microbial discovery

The AI architectures in microbial discovery are the frameworks within which different AI methods are deployed. These frameworks are method-based and can also be described as a long, rigorous process through which AI methods have evolved over time in, learning from biological data or microbial systems and predicting outcomes from applications like metabolic engineering, strain optimization and disease diagnostics. This framework can also be described as a multilayered pipeline that extracts actionable insights from raw microbial data. For instance, the transition from simple analytics to predictive biotechnology is driven by advanced machine learning and deep learning architectures. In this section, we shall discuss the applicability of some of these methods in microbial biotechnology.

2.1 Deep learning

Deep learning is a subset of machine learning that trains the computer to model the human brain in handling tasks. Methods such as convolutional neural networks (CNNs) and transformers are pivotal to decoding non-linear genomic interactions, and accelerating protein structure predictions, have been widely applicable in the fields of proteomics and genomics (Agrawal et al., 2018; Gong et al., 2025). CNNs are particularly useful in tools like AutoCRISPR that are used in popular clustered regularly interspaced palindromic repeats (CRISPR) experiments to predict off-target effects of genome edits, which significantly reduces the guide RNA design cycles from months to weeks and also ensures reasonable precision in guide RNA activity and a higher likelihood of editing success (Priyadarshini et al., 2024).

Furthermore, deep learning models are instrumental to unravelling microbial genetic circuits and metabolic pathways, enabling the design of optimized microbial cell factories for enhanced bioproduction (Ajikumar et al., 2010). This includes facilitating enzyme selection and pathway design for innovative smart cell factory architectures and enabling swift and precise adjustments to microbial robustness (Mao et al., 2024). AI-driven approaches are also a popular option for fast-tracking the functional annotation of microbial genes, addressing the challenge of having a significant proportion of genes with unknown functions even in sequenced genomes (Palsson et al., 2026). For instance, graph-based featurization extracted from metagenomic data, processed by CNNs, can elucidate the metabolic functions of microorganisms and their interactions within complex systems, such as activated sludge, thereby linking cellular activities to broader process-level impacts (Sin and Al, 2021).

Moreover, CNNs and recurrent neural networks (RNNs) can accurately classify microbial strains based on genomic sequences for traits like antimicrobial resistance and predict susceptibility from other data, such as treatment history or microbial evolution over time (de la Lastra et al., 2024).

2.2 Predictive analytics

AI techniques like random forest (RF) and artificial neural networks (ANNs) are machine learning methods with many applications in biological sciences. RF is an ensemble-based method that uses many decision trees for predictive purposes and is very powerful in handling structured data. Conversely, ANNs, especially deep learning models, excel at processing high-dimensional and complex data, such as genetic sequences and real-time monitoring data, to generate predictive models for various biological phenomena and even predict chemical structures from sequence information (Naseri, 2023; Nwankwo et al., 2024). These models can accelerate drug discovery pipelines and optimize biocatalytic processes by inferring structure-function relationships from large datasets. They are also applicable to precise taxonomic profiling and identifying bacterial phenotypes, such as antimicrobial resistance (Aghdam et al., 2024).

These predictive models facilitate high-throughput microbial culturomics and the automated isolation of microbial communities, significantly advancing beyond traditional cultivation limits (Selma‐Royo et al., 2023). In microbial population genomics, sophisticated machine learning models often integrate statistical inference with whole-genome approaches to predict biologically relevant features, analyze variable impacts, and identify intricate data patterns (Sheppard et al., 2025). These models can predict the phenotypic characteristics of microorganisms, including their metabolic capabilities and resistance profiles, directly from genomic sequences, thereby improving the vital processes of therapeutic interventions (de la Lastra et al., 2024). Such predictive capabilities also extend to forecasting microbial community structure and temporal dynamics, moving beyond established cause-effect relationships to leverage computational advances (Andersen et al., 2025).

2.3 Large language models

Large language models (LLMs) are emerging AI agents that are now broadly applied to automate end-to-end machine learning experimentation on complex biological datasets, moving the field toward autonomous scientific discovery (Sarumi and Heider, 2024). Such agents include Omega, an LLM designed to facilitate bioimage analysis from experimental design to implementation; BioImage; and input/output (IO) chatbots that use community knowledge to navigate complex bioimage analysis tasks through written prompts (“Embedding AI in Biology”, 2024). These agents integrate LLMs to interpret complex biological queries, suggest experimental parameters, and even generate code for image processing and analysis, thereby accelerating research workflows and making sophisticated bioimaging techniques more readily accessible (“Embedding AI in Biology”, 2024). LLMs in microbial biotechnology can analyze intricate genomic and proteomic data to forecast novel functions of microbial proteins, particularly concerning their significant industrial applications. Unlike the traditional methods, this approach can fast-track the discovery of industrially valuable compounds, such as enzymes that are stable at high temperature and pH, via sophisticated computational techniques that expedite the identification of gene functions, especially through structure-based strategies and multidimensional metagenomics (Hammack and Blaby‐Haas, 2023; Khan and Khan, 2026; Palsson et al., 2026).

This sophisticated analytical capability facilitates the identification of elusive metabolic pathways and the discovery of potential biocatalysts from various microbial ecosystems. These models facilitate a multiscale understanding of microbial function and regulatory dynamics by integrating multi-omics data, extracting salient features, and classifying complex host-microbiome interactions (Tian and Zhao, 2025). Furthermore, machine learning algorithms, such as gradient tree boosting and ANNs, are being successfully applied to predict pH preferences and antimicrobial resistance directly from gene sequences, broadening our understanding of microbial adaptability and susceptibility (Sheppard et al., 2025). These advancements also include the discovery of novel antimicrobial compounds, where models can predict the potency of antimicrobial peptides and even suggest modifications for enhanced efficacy (Wang et al., 2025).

Specifically, LLMs are also being adapted to function as specialized tools for high-throughput mining and generation of novel antimicrobial peptides, enabling rapid screening of potential candidates against drug-resistant bacteria (Wang et al., 2025). This approach allows for the efficient and extensive exploration of antimicrobial peptide chemical space for the discovery of novel antimicrobials against multidrug-resistant pathogens like the non-fastidious Pseudomonas aeruginosa, a known major nosocomial pathogen (Wang et al., 2025; Oyeniran et al., 2026). Figure 2 shows the graphic illustrations of the AI architectures in microbial biotechnology and the existent interplay between its various method-based application frameworks.

3 Engineering the future: precision fermentation and cell factories

AI is reinventing the design-build-test-learn cycle, transforming microbial engineering from manual trial-and-error to high-throughput autonomous systems (Chen et al., 2025; Gurdo et al., 2023). Numerous applications yielding favorable outcomes are now gaining wider acceptance. Thus, the future of biological research devoid of AI-driven innovations appears untenable, considering the evident correlation between biology and mathematics, particularly in the many applications from mathematics to biological studies. The advancement of microbial technology represents a multidisciplinary endeavor, synergizing mathematical and computational disciplines to optimize the functional capabilities of microorganisms. In this section, we shall discuss various AI-aided ways in which microbial biotechnology is being engineered for the future.

3.1 CRISPR gene editing optimization

CRISPR-Cas systems are a foundational element of contemporary microbial engineering that facilitates precise and programmable genome editing to implement targeted modifications that strengthen metabolic pathways and bioproduction efficiencies. When integrated with AI, such as in the DeepCRISPR tool for guide RNA design and predictive modeling of editing outcomes, it streamlines CRISPR optimization, accelerating the design-build-test-learn cycle by up to 70% in design efficiency (Burbano et al., 2024). This enables applications such as the optimization of Bacillus altitudinis peptidase thermostability from 85 °C to 92 °C for fruit pulp digestion via AlphaFold-predicted structures, the identification of acid-tolerant Lactobacillus plantarum strains in fermented matrices using RF models, and the enhancement of probiotic adhesion through single-cell CRISPR screening integrated with machine learning (Priyadharshini et al., 2025). Furthermore, the convergence of AI and synthetic biology allows for the metabolic flux engineering of lactic acid bacteria to optimize the biosynthesis of high-value nutraceuticals like nicotinamide mononucleotide (Bustos and Gerez, 2026).

Optimising CRISPR-based gene editing for metabolic engineering is forward-thinking, as it can benefit greatly from advanced AI models capable of predicting optimal single guide RNA (sgRNA) sequences and off-target effects, significantly enhancing the precision and efficiency of genetic manipulations for industrial bioprocesses. The application of deep learning to CRISPR-based systems includes the design of novel antimicrobial peptides, where models like HydrAMP (a deep learning model designed to generate and optimize antimicrobial peptides) generate potent candidates against antibiotic-resistant strains, transcending traditional sequence-activity relationships and enabling the exploration of the peptide sequence space for better results (Liu et al., 2024). In the like manner, AI-driven approaches, including variational inference autoencoders, are also being used to design novel antimicrobial peptides by modeling peptide generation as a density modeling problem that samples high-probability regions of the sequence space to identify active compounds (Liu et al., 2024).

Furthermore, AI-driven advancements have simplified the daunting nature of microbial cell factories via improved expression and product delivery. For instance, deep learning models trained on Escherichia coli genomic and transcriptomic data have identified synthetic promoter-gene combinations that substantially enhance metabolic flux and protein titers while minimizing the metabolic burden on the host cell (Wang et al., 2020; Zhang et al., 2023). Similar AI-driven frameworks in Bacillus subtilis have been leveraged to predict cellular stress responses and optimize global regulatory networks, facilitating the engineering of robust bacterial strains for high-yield bioproduction (Tibocha‐Bonilla et al., 2022; Xia et al., 2024).

This precise AI-driven metabolic engineering facilitates the creation of resilient cell factories with reduced cell-to-cell variation, tackling a major obstacle in attaining consistent high-yield bioproduction (Mao et al., 2024). Lastly, integrating generative AI methodologies with extensive biomedical databases can expedite the development of innovative therapeutic molecules and peptides with improved biological significance and customized attributes.

3.2 Metabolic modeling

Metabolic modeling is the use of a computational framework to predict the behavior of metabolic networks under various conditions, enabling the identification of optimal pathways for bioproduction. These conditions may include substrate availability, nutrient limitations, and genetic perturbations, which can be analyzed to optimize microbial cell factories for improved yield and specificity of target compounds. Computational frameworks for metabolic modeling often incorporate kinetic models for simulating metabolic responses to genetic perturbations, thereby streamlining the rational design of microbial strains for targeted bioproduction (Narayanan et al., 2024). When these models are supercharged with machine learning algorithms, they can almost accurately predict genotype-to-phenotype relationships, fine-tuning the optimization of complex metabolic pathways for targeted products (Zhang et al., 2020). In addition, these models can quickly resolve high-dimensional biological data to optimize complex biosystems, ranging from individual proteins to entire metabolic pathways (Hisberg et al., 2019).

Deep learning-based frameworks, such as the reconstruction of kinetic models using deep learning (REKINDLE), support the generation of kinetic models that accurately characterize intracellular metabolic states, solving the kinetic model’s problem of data scarcity and strengthening our knowledge of metabolic regulation (Choudhury et al., 2022). Hybrid models like MetaFlux integrate genomic, proteomic, and metabolomic data to refine flux balance analysis, allowing for a more complex understanding of metabolic bottlenecks and the prediction of optimal genetic modifications for bioproduction (Latendresse et al., 2022). These models amalgamate mechanistic biological principles with empirical data-driven methodologies to develop “digital twins” of microbial factories, optimizing fermentation parameters to enhance the yield and purity of functional ingredients such as engineered enzymes, vitamins, and antioxidants. Additionally, they enable researchers to theoretically investigate genotype-phenotype relationships prior to undertaking costly wet-lab experiments (Butean et al., 2025; Ham et al., 2025).

Overall, AI integration into metabolic modeling streamlines the optimization of metabolic pathways and host strains while promoting the discovery of novel biosynthetic pathways and the rational design of microbial cell factories for the sustainable bioproduction of valuable compounds for industry, health and the environment (Gao et al., 2025; Generating smarter biotechnology, 2023).

Moreover, the utilization of machine learning in synthetic biology and metabolic engineering has resulted in substantial enhancements in the synthesis of target compounds, including tryptophan, via optimized strain design and fermentation methodologies (Zhang et al., 2020). These modeling techniques are central to the development of next-generation cell factories, making system- and genome-wide optimization of metabolic networks a reality.

3.3 Automation and biofoundries

Biofoundries are automated platforms that integrate AI with robotic systems to innovate and accelerate the design-build-test-learn cycle for biological engineering, enabling rapid prototyping and optimization of microbial strains for industrial applications. Key components of biofoundries are high-throughput genetic manipulation, advanced analytical instrumentation for phenotypic characterization, and sophisticated computational tools for data interpretation and predictive modeling (Narayanan et al., 2024). Biofoundries are also instrumental in enabling agile manufacturing, where flexible and adaptable processing approaches can rapidly shift production to different feedstocks and products, minimizing the time between harvest and processing for diverse bioresources (Gericke et al., 2023).

High-throughput genetic manipulation is central to biofoundries, enabling the rapid construction and screening of vast libraries to accelerate metabolic engineering. Additionally, advancements in automation, ultrahigh-throughput screening, and quantum computing are unlocking unprecedented potential in biocatalysis research (Yi et al., 2021; Mao et al., 2024). However, despite these technological advancements, a significant challenge of accurately predicting enzymatic parameters for all enzymes within a metabolic pathway remains, necessitating high-throughput methods like those offered by modern biofoundries for improved model accuracy (Yu et al., 2023).

Advanced analytical instrumentation, such as cryo-electron microscopy and serial femtosecond nanocrystallography (SFX), further refines the structural and dynamic information of biocatalysts, contributing to reduced timeframes for smart and safer biocatalyst library design (On Advances and Challenges in Biocatalysis, 2018). Biofoundries often integrate active machine learning workflows, such as METIS, to efficiently optimize biological targets with minimal experimentation, demonstrating significant enhancements in system performance across diverse applications (Pandi et al., 2022). This integrated automation also includes comprehensive phenotyping, leveraging automated microbioreactor platforms and advanced data processing for reliable characterization of engineered strains (Orsi et al., 2024).

Furthermore, biofoundries, with centralized facilities that adopt rigorous automation and AI, are accelerating the creation of next-generation microbial chassis for chemical and food production (Driessen, 2022; Kanwal et al., 2026). These facilities leverage robotics, high-throughput screening, and AI-driven data analysis to systematically design, build, test, and learn from microbial systems, thereby enabling the rapid iteration and optimization of biotechnological processes (Pretorius et al., 2025). This approach may show promise in significantly reducing the time and cost associated with developing new microbial strains and bioprocesses, fostering rapid innovation in biotechnology.

The integration of machine learning with automated biofoundries and ongoing in vivo evolution enhances the optimization of biocatalysts, minimizes human error, and promotes scientific advancements (Orsi et al., 2024). This technique allows for the high-throughput screening and sorting of microbial strains based on multi-modal phenotypes, overcoming a major bottleneck in microbial cell factory development (Diao et al., 2025). Such AI-powered frameworks advance precision fermentation a cutting-edge method leveraging synthetic biology and metabolic engineering to engineer microbial cell factories for high-value food ingredients and bio-based products, revolutionizing global food production (Choi and Lee, 2023; Gao et al., 2025).

The integration of AI with biotech research, like in CRISPR editing, metabolic modeling and automated biofoundries, is set to significantly enhance the iterative design, construction, testing, and learning processes in biotechnology, ushering microbial engineering into a new era of efficiency and innovation. Figure 3 shows a graphical illustrative summary of this process.

4 AI in soil health and crop resilience

The soil, as the universal reservoir of microorganisms, sits at the interface of plant resilience and environmental health, playing a critical role in nutrient cycling, disease suppression, and overall ecosystem stability. Soil health is profoundly influenced by microbial activity, directly impacting nutrient cycling, disease suppression, and overall plant vigor, which are critical for sustainable agriculture and food security (Aguilar et al., 2025). Nutrient cycling is a key process that is mediated by microbial communities to maintain the intricate balance and continuity of the complex interactions governing nitrogen fixation, phosphorus solubilization, and organic matter decomposition in the cycle (Elufioye et al., 2024).

Disease suppression, a desirable characteristic of improved plants, is substantially affected by microbial consortia and can be augmented through AI-assisted identification of advantageous microorganisms and their metabolic pathways, resulting in targeted biocontrol strategies that reduce pathogen effects on crops. Crop resilience and soil health are closely linked, with robust microbial ecosystems contributing significantly to the crops’ ability to withstand environmental stressors (Pearson et al., 2022). Microbial communities, despite being the unsung heroes of soil health, have a predictive potential for crop productivity that still remains largely understudied (Aghdam et al., 2024; Iqbal et al., 2025). Interestingly, AI can be deployed here to analyze extensive datasets comprising climatological information, soil moisture, microbiome and crop genetic data to predict agricultural outcomes under varying climatic scenarios and propose optimal adaptation strategies (Zidan and Febriyanti, 2024; Wang et al., 2026). We shall discuss this application as follows:

4.1 Bioprospecting: biofertilizer and biocontrol discovery

Biobased products are usually derived from biological sources that serve as green or more sustainable alternatives to the current popular practices. The most important include biofertilizers and biopesticides, which harness beneficial microorganisms to bolster nutrient availability and suppress pathogens, offering sustainable alternatives to synthetic agrochemicals (Graham and Ledesma‐Amaro, 2023). Presently, biofertilizers are gaining significant traction, with a projected 12.8% market growth from 2020 to 2027, driven by the need to reduce costly and environmentally damaging nitrogenous fertilizers (Brooks and Alper, 2021). Sustainable agricultural practices now strongly emphasize the enhancement of plant immunity through symbiotic microbial interactions. Such strategies foster physiological robustness and crop resilience while concomitantly mitigating the environmental impacts on conventional farming. This is demonstrated by the application of nanomaterials to alter soil microbiome signaling and community architecture, thereby facilitating improved nutrient bioavailability (Emmenegger et al., 2023; Zhang et al., 2021).

Farmers are increasingly adopting these microbial inoculants, which demonstrably improve crop productivity and contribute to sustainable agricultural practices (Compant et al., 2024). To optimize these microbial applications, integrated biotechnological and AI innovations encompassing ‘omics’ technologies, genome editing, and high-throughput phenotyping are critical for crop improvement (Li et al., 2025). This interdisciplinary approach often facilitates the targeted recruitment of crop-beneficial soil taxa through network analysis of metagenomic data, leading to a reduction in agrochemical use and the adoption of better eco-friendly sustainable solutions (Berihu et al., 2023).

AI techniques, such as supervised machine learning models, have been used to predict crop productivity using soil metagenomic data with an accuracy of 0.79 (Iqbal et al., 2025). This is also applicable to identifying optimal microbial consortia for enhanced nutrient cycling, improved growth and disease suppression, moving beyond simple correlational analysis to mechanistic insights into soil microbiome function (Schaefer, 2023). Furthermore, AI-supported bioinformatics and computational genomics frameworks can simplify gene prediction and functional annotation and unravel genetic diversity and identify adaptive traits crucial for crop improvement through the development of robust, stress-tolerant varieties (Udegbe et al., 2024; Li et al., 2025).

Additionally, AI algorithms aid in forecasting disease outbreaks and pest infestations by integrating real-time environmental data with historical patterns, enabling proactive intervention strategies (Elufioye et al., 2024). Biocontrol strategies that include agents like microbial antagonists or predatory insects to suppress pathogen populations have also been optimized with AI, their deployment based on predictive models of pest dynamics and environmental conditions (Javed et al., 2025). The integration of AI with remote sensing technologies and internet of things (IoT) sensors can further aid precision agriculture, enabling real-time monitoring and adaptive management of microbial interventions for optimal efficacy (Zidan and Febriyanti, 2024).

Furthermore, AI can integrate diverse data layers, including metabolomics and transcriptomics and images where applicable, to provide a more holistic understanding of microbial interactions and their impact on cropping systems (Berihu et al., 2023). This comprehensive data integration and simulation enables real-time decision support for optimal agroecosystem management, including selection of optimal seed varieties at the farm level (Zhang et al., 2021). It also fosters dynamic adjustments in biofertilizer and biopesticide application, ensuring precise delivery where and when needed, thereby minimizing waste and maximizing beneficial impacts on crop health and yield (Zhang et al., 2021).

Lastly, this capability is important for optimizing the application of biological agents, reducing reliance on synthetic chemicals, and enhancing food security and agricultural sustainability through the green initiative.

4.2 Drought and stress identification

Environmental stressors like drought, salinity, and extreme temperatures significantly impact agricultural productivity by altering plant physiology and microbial community dynamics. Economic losses from such stressors can be substantial, with statistics of over $200 billion annually making it even more urgent to identify better alternatives like AI-driven solutions for enhancing crop resilience and mitigating adverse effects (Schaefer, 2023). Climate change further exacerbates these challenges, necessitating advanced strategies for rapid and accurate stress detection and mitigation. The first step to address these challenges is to accurately identify and quantify plant stress levels, which AI, through better sensor technologies and machine learning algorithms, can achieve by analyzing high-resolution imagery and diverse sensor data (Schaefer, 2023). Deep learning AI models can interpret complex patterns in multispectral and hyperspectral imaging, distinguishing between various stress types such as nutrient deficiencies versus pathogen attacks with high precision (Paçal et al., 2024). This facilitates the creation of targeted interventions that improve crop resilience and productivity in response to abiotic and biotic stresses, an essential measure for lessening climate change effects on agriculture (Gilbert and Zengler, 2025). This approach allows for the prompt identification of stress signals, thereby facilitating timely interventions to reduce crop damage and enhance resource distribution.

Furthermore, integrating real-time physiological plant data from advanced sensing platforms such as optical probes and electrochemical sensors enables AI to monitor key biomarkers of stress responses like changes in chlorophyll fluorescence, stomatal conductance, and metabolite levels. This approach facilitates localized and timely interventions for precision farming (Bukhamsin et al., 2025). Techniques incorporating non-destructive near-infrared-II fluorescent nano sensors integrated with supervised machine learning models can attain high precision in identifying plant stress-signaling molecules such as hydrogen peroxide, providing dependable optical instruments for precision agriculture and early disease detection (Hu et al., 2025). This advanced sensing capability, including the use of single-walled carbon nanotube-based optical nano sensors, is crucial for discerning early plant stress responses, thereby aiding in the development of climate-resilient crops (Ang et al., 2024; Hu et al., 2025). Nanotechnological approaches can promote better observation of distinct dynamics and temporal wave characteristics of stress-related molecules, providing critical insights into signaling mechanisms for developing more robust plant varieties.

In addition, interpretable machine learning now decodes how soil microbiomes respond to environmental stressors like drought and identifies specific root microbes that promote resilience (Hagen et al., 2024). This can further facilitate the targeted modulation of microbial communities to enhance plant stress tolerance and improve crop yields in challenging conditions. Beyond optimizing individual microbial strains, AI-driven analyses of remote sensing data using machine learning algorithms are instrumental in mapping soil variability and diagnosing critical parameters such as salinity, moisture dynamics, and nutrient content across vast agricultural landscapes (Howari, 2026).

These advanced analytical capabilities are also applicable to achieving a dynamic adjustment of irrigation schedules and nutrient delivery, minimizing resource waste and maximizing crop productivity under varying environmental conditions (Zidan and Febriyanti, 2024). Interestingly, farmers can leverage these technologies to apply resources like herbicides and fertilizers only where needed, reducing environmental impact and improving efficiency. Figure 4 shows the graphical summary of AI in soil health for bioprospecting environmentally friendly fertilizer and pesticides in early stress identification and management.

5 Current challenges and strategic bottlenecks

Despite these advancements, the full integration of AI into microbial biotechnology still suffers from several systemic hurdles, challenges and limitations.

5.1 Data quality and scarcity

Robust AI models require large-scale labelled datasets, which are currently scarce for non-model organisms (Kim, 2019). Non-model organisms often lack genomic annotation and standardized phenotypic data, making the development of reliable predictive algorithms for such organisms difficult (Koca and Akan, 2025). The scarcity of this labelled dataset is primarily due to significant environmental heterogeneity and the lack of standardized protocols for high-throughput culturomics in various agricultural soils (Huang et al., 2023; Lei et al., 2023). Bacillus strains isolated from hyper-arid ecosystems are examples of such non-model organisms, where the disparity between laboratory-grown cultures and complex field conditions hinders the generalization of machine learning predictions (Burz et al., 2023). This scarcity is particularly more common in microbiome research, where the complexity of microbial communities and their interactions requires high-resolution, multi-omics data that is often expensive and time-consuming to acquire (Jurburg et al., 2022).

Furthermore, the standardization of experimental protocols and data collection methods for quality is crucial to generating interoperable datasets that can effectively train working AI algorithms for predictive modeling in diverse agroecosystems (French et al., 2021). Given the sensitive nature of AI algorithms to non-standardized data, the presence of noise or incompatible input formats can significantly degrade model performance, leading to unreliable predictions that might misguide important agricultural decision-making (Kim and Heo, 2024). The heterogeneity of environmental conditions and the transient nature of microbial populations may further complicate the establishment of consistently reliable and generalizable AI models (Burz et al., 2023).

It is also worth mentioning that the development of predictive models may be further impaired by the incomplete understanding of microbial metabolic pathways and the dynamic nature of microbial community interactions, thereby requiring a close collaboration between biologists and computer scientists. In fact, it is now more important than ever for scientists to be knowledgeable in these fields to construct more accurate and robust, scalable AI models adept at predicting microbial behavior and optimizing biotechnological applications in complex agricultural settings.

5.2 The “black box” problem

Deep learning models are often difficult to interpret, meaning they provide predictions without a clear explanation of the underlying biological concept (Wang et al., 2025; Zhu et al., 2020). Understanding the underlying biological concepts is essential for validating scientific rationale, yet the opacity of these models often obscures the mechanistic drivers of microbial behavior. The absence of transparency can diminish trust in clinical and agricultural decision-making and hinder the validation and enhancement of AI-driven solutions, particularly in contexts where comprehending the mechanistic foundations of microbial activity is crucial for optimizing agricultural practices or creating innovative biotechnologies (Ali and Mohammed, 2023). To address this challenge, the integration of kinetic modeling with multifaceted biological expertise may yield the requisite interpretative depth and mechanistic understanding essential for intricate industrial applications (Zhang et al., 2024). Complex industrial applications benefit from incorporating interpretability frameworks, such as feature importance assessment and partial dependence analysis, to transition these systems from purely opaque black-box models toward transparent, actionable insights (Gupta et al., 2024). While multidisciplinary biological knowledge is useful for interpreting these outputs, the adoption of interpretable machine learning remains a critical necessity to ensure that biological discoveries are grounded in causal mechanisms rather than mere statistical correlations.

5.3 Computational and economic costs

High-throughput automation and complex structural simulations require significant capital investment and computational resources, which is a major challenge for resource-limited research teams (Driessen, 2022; Zhang et al., 2024). In sub-Saharan Africa, for instance, computational resources remain a big constraint of bioinformatics, given the low-resource status of many countries in this region; it is nearly impossible to properly train students and budding scientists in computational biology. These expenses are further compounded by the continuous need to upgrade hardware and software infrastructure to keep pace with rapid advancements in technology and the increasing volume of complex biological data. This issue is particularly pertinent for scaling AI applications from laboratory datasets to industrial operations, where extrapolation can lead to increased prediction uncertainty and instability in control systems (Gupta et al., 2024). Training deep learning models requires substantial financial and computational resources, creating a significant barrier to entry, particularly for smaller entities. These costs are often prohibitive; for example, training GPT-3 required approximately 355 GPU-years at an estimated cost of $4.6 million (Sapoval et al., 2022; Shen et al., 2024). Given the extensive parameter counts and vast datasets involved, training these architectures from scratch is frequently economically unviable, especially when scaling for specialized downstream applications.

Furthermore, the overall economic overhead significantly increases due to the computational burden of iterative model refinement and the constant need for retraining with new data to maintain performance in dynamic microbial ecosystems. Bridging this economic divide necessitates the development of more resource-efficient AI algorithms and accessible computational platforms.

5.4 Ethics and governance

As AI-driven laboratories become more autonomous, ethical considerations regarding data misuse, biosafety, and the socio-economic impacts of displaced manual labor must be addressed (Driessen, 2022; Tom et al., 2024). The situation also raises significant ethical questions regarding data privacy, intellectual property, and equitable access to beneficial technologies, which requires the development of comprehensive regulatory frameworks. Also, if inherent biases in training datasets are not meticulously addressed, they could either lead to discriminatory outcomes or perpetuate existing inequalities within the applicable agricultural and food systems (“Generating ‘smarter’ biotechnology, 2023). A proactive development of robust governance structures and regulatory guidelines that ensure responsible innovation and equitable access to AI-powered biotechnological advancements may help address this concern. Furthermore, the establishment of clear standards and regulatory frameworks is critical to promoting the responsible and widespread adoption of AI in most areas, including agriculture (Elufioye et al., 2024).

Most regulatory frameworks, however, are often post-hoc, necessitating costly resubmission processes that hinder rigorous ethical evaluations during early developmental stages (Weiner et al., 2025). The delay in regulatory adaptation constrains technological progress and intensifies the potential for unforeseen ethical complexities as AI applications within microbial biotechnology become deeply integrated into critical infrastructure. In addition, the rapid velocity of AI evolution frequently exceeds the capacity of regulatory bodies to formulate comprehensive guidelines, thereby denigrating the assurance of responsible and ethical deployment (Ibegbulam et al., 2023). Ultimately, navigating the evolving ethical landscape of advanced AI in microbial biotechnology needs a fundamental shift toward proactive and adaptive regulatory mechanisms.

6 The road ahead: human-AI collaboration and self-driving laboratories

The future of microbial biotechnology lies in self-driving laboratory systems that integrate robotic production with AI for autonomous experimentation. However, the most practical near-term trajectory involves hybrid models that combine AI-driven decision-making with sustained human oversight (Helleckes et al., 2026).

This “human-in-the-loop” approach ensures that biological complexity and regulatory requirements are reasonably managed while leveraging the speed and precision of automated systems (Branda and Ciccozzi, 2026; Helleckes et al., 2026). This collaborative system maintains a balance between innovation and rigorous ethical considerations, particularly regarding potential biases in AI systems and the need for explainable outputs in complex decision-making processes. This iterative human-AI interaction is important for refining predictive models and validating experimental outcomes, particularly in sensitive areas like biosecurity where autonomous AI models could pose substantial risks (Bloomfield et al., 2024). Such risks may include the unintentional synthesis of hazardous biological agents or the uncontrolled changes in microbial phenotypes requiring the implementation of rigorous safety protocols and human-directed ethical oversight (Gao et al., 2024; de la Lastra et al., 2024). Thus, the implementation of a graduated autonomy framework can make these systems perform specific tasks in a limited capacity, with the potential for expanded operational scope contingent upon safety, while performance metrics are rigorously validated (Tang et al., 2025).

Such oversight is critical to handling vulnerabilities in AI systems, including their resistance to malicious attacks and their capacity for risk awareness, especially as AI scientists gain more autonomy (Tang et al., 2025). Furthermore, the increasing sophistication of generative AI in biotechnology also requires the implementation of built-in, AI-native safeguards, such as watermarking and anti-jailbreak methods, to mitigate biosecurity risks like the accidental enhancement of virulence, gain-of-function, and the design of toxins (Wang et al., 2025). A proactive security strategy is essential, as emerging biological models, although promising for therapeutic advancements, also pose the inherent risk of being repurposed to develop novel pathogens with increased virulence or transmissibility (Bloomfield et al., 2024).

Consequently, a robust safety framework for AI in scientific discovery should foster a three-pronged relationship among humans, machines, and the environment, incorporating dynamic environmental feedback alongside human oversight to ensure behavioral safety in changing contexts (Tang et al., 2025). An approach that combines human expertise with AI’s analytical power in this manner is essential for navigating the intricate landscape of microbial biotechnology, ensuring both innovation and responsible application within the food security domain (Gilbert and Zengler, 2025). Moreover, the implementation of human-in-the-loop systems can effectively mitigate issues arising from out-of-the-loop scenarios, where AI systems operate autonomously without sufficient human intervention or understanding. This can further strengthen reliability and diagnostic accuracy, especially in critical applications with sparse training data.

7 Conclusion

The intersection of AI and microbial biotechnology signifies a transformative paradigm shift in bolstering global food security. By streamlining CRISPR-based genome engineering, accelerating the identification of biocontrol agents, and refining soil health management, AI-driven platforms are fundamentally expanding the parameters of agricultural productivity. Surmounting the attendant challenges of data standardization, model interpretability, and substantial computational expenditure remains an urgent frontier for the field. Successfully addressing these setbacks is a prerequisite for the global deployment of dependable, scalable, and ethical biotechnological solutions. The sustained efficacy of microbial biotechnology is contingent upon the iterative optimization of machine learning architectures to enhance their predictive capabilities. The integration of advanced computational frameworks, including transfer learning, reinforcement learning, and generative adversarial networks, serves to minimize predictive discrepancies within metabolic models and enables the identification of superior microbial strains for drought resilience, growth and yield improvement with greater efficiency than conventional approaches. Furthermore, the implementation of dataset-orientated model selection alongside hybrid frameworks, which fuse mechanistic biological understanding with data-derived insights, may also allow investigators to reduce overfitting while simultaneously bolstering the stability of bioprocess control. Ultimately, these methodological advancements, coupled with continuous human oversight for responsible implementation, mitigate potential misuse or unforeseen systemic repercussions. They are essential for fully leveraging microbial capabilities to meet the nutritional needs of a growing global population.

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Footnotes

Edited by: Qingfang He, University of Arkansas at Little Rock, United States

Reviewed by: Lina Blandon, National University of Colombia, Colombia

S Adeeb Mujtaba Ali, Deccan College of Medical Sciences, India