Artificial intelligence key to advancing food security through microbiome analysis

Artificial intelligence is enabling breakthroughs in microbiome science that span human health, animal husbandry, and aquaculture. A new review published in Applied Sciences offers the most comprehensive analysis to date of how machine learning, deep learning, and explainable AI are being deployed across these interconnected fields.

The study, titled “Artificial Intelligence in Microbiome Research and Beyond: Connecting Human Health, Animal Husbandry, and Aquaculture”, points out the growing reliance on AI to decode complex microbial communities, enhance food system resilience, and uncover links between gut ecosystems and overall health.

How is AI transforming human microbiome research?

The review finds that the human health sector is currently the most advanced in applying AI to microbiome analysis. Researchers are deploying supervised and unsupervised learning, deep neural networks, and dimensionality-reduction tools to classify patients, predict diseases, and map microbial dysbiosis.

Machine learning algorithms have been used to predict cancer progression, identify early signs of Parkinson’s disease, and assess comorbidities such as heart failure and depression by combining metagenomics with metabolomics. Random forests, support vector machines, long short-term memory networks, and gradient boosting ensembles dominate the field, often combined with principal component analysis to manage high-dimensional datasets.

Interpretability has become a central concern, and explainable AI methods such as SHAP values are increasingly applied to rank the importance of specific microbial taxa. This makes results not only accurate but also biologically meaningful, addressing the common critique that AI systems act as “black boxes.” Unsupervised approaches, including partitioning around medoids and clustering frameworks, are equally important for identifying community structures such as distinct pregnancy-related microbial clusters or dietary and allergy-associated dysbiosis.

The review highlights the discovery that gut microbial compositions differ significantly between humans and fish, with Firmicutes and Bacteroidetes dominating human gut ecosystems, while Firmicutes and Proteobacteria are more common in fish. Zebrafish models are proving valuable in bridging these insights, while emerging studies of resident microbiota in salmonid brains open the door to questions about similar phenomena in humans.

What role does AI play in animal husbandry?

In livestock farming, the application of AI to microbiome data is still in its early stages but is showing rapid growth. The review details progress in ruminants, swine, and poultry, where predictive models are being used to improve productivity, efficiency, and disease resistance.

For ruminants, random forest models have been applied to link fecal microbiota to milk urea nitrogen content, while deep learning tools are being designed to identify fungal and protozoan sequences in rumen metagenomes. These insights help optimize feeding strategies and nutrient utilization, directly improving farm economics.

Swine studies have demonstrated the power of machine learning to identify breeding status and growth potential. Gradient boosting combined with SHAP interpretability has revealed how eukaryotic communities influence body weight, while clustering approaches have uncovered microbial enterotypes associated with nitrogen-use efficiency. In addition, BERT-based natural language processing models are being adapted to annotate viral operational taxonomic units, expanding the scope of microbial surveillance in pigs.

In poultry, transformer-based algorithms are being employed to predict the prevalence of pathogens, while ensemble trees are assisting in the prediction of antimicrobial resistance phenotypes from metagenomic sequences. These methods are vital for addressing the global challenge of antibiotic resistance while maintaining high standards of poultry health and food safety.

Although progress is evident, the authors note that AI-driven microbiome research in animal husbandry lags behind human applications. The key challenge remains limited datasets, with less standardization across farms, breeds, and geographies. Without greater reproducibility and sharing of both data and code, scaling AI across livestock industries will remain constrained.

Can aquaculture harness AI for microbiome and sustainability gains?

Aquaculture, one of the fastest-growing food sectors worldwide, represents a frontier for AI in microbiome research. While applications are less mature compared to human and livestock studies, early examples point to transformative potential.

The review highlights studies where random forest models have successfully detected compromised aquatic environments by analyzing gut microbiota in fish. Partitioning around medoids clustering has been used to distinguish benthic from pelagic community structures, while Bayesian networks have linked microbial profiles to aquaculture conditions such as feed type and water quality.

Beyond microbiome-specific studies, AI is driving innovation across aquaculture management. Computer vision and deep learning architectures such as YOLO and convolutional neural networks are enabling real-time parasite detection and feeding behavior classification. Hybrid systems combining near-infrared computer vision with adaptive neuro-fuzzy inference are optimizing feeding schedules. Gradient boosting and meta-analysis approaches are being applied to feeding frequency optimization, while multi-omics coupled with random block methods are predicting feed efficiency and growth outcomes. Random forest models are further being deployed to predict weight dispersion in farmed fish populations.

These applications illustrate how AI can simultaneously enhance productivity and sustainability in aquaculture. By automating monitoring, predicting health risks, and optimizing feed use, the sector could significantly reduce environmental impacts while meeting rising global demand for protein.

Challenges and future directions

The review identifies persistent challenges that span all sectors. Data heterogeneity, lack of standardization, class imbalance, and the risk of information leakage remain barriers to building robust models. Hyperparameter tuning and limited validation across diverse environments also limit scalability. Importantly, while data sharing has improved, open access to code remains rare, reducing the reproducibility of results.

The authors argue that future progress will depend on integrating AI with the Internet of Things, edge and cloud computing, and digital twin systems. These technologies could allow for real-time microbiome monitoring in humans, animals, and aquaculture environments. Another priority is validating AI findings through biological experiments to ensure that predictions translate into practical interventions. Finally, transferable frameworks, where algorithms refined in one species or ecosystem can be adapted to another, are expected to drive efficiencies and accelerate adoption across domains.