ReviewFrontiers in microbiomes2026
Artificial intelligence in soil microbiome-driven agriculture: from practical limits to a translational roadmap.
Review in Frontiers in microbiomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Soil microbiome research has been revolutionized by advances in high-throughput sequencing and multi-omics technologies, generating massive datasets that capture the taxonomic, functional, and metabolic diversity of microbial communities in agricultural soils; however, interpreting these complex datasets and translating them into practical agronomic insights remains challenging. Objectives: To critically assess the role of artificial intelligence (AI) in soil microbiome-driven agriculture, focusing on methodological developments, prediction performance, existing limitations, and translational opportunities. Methods: A narrative review was conducted to evaluate commonly used AI approaches, including random forest, gradient boosting, support vector machines, and deep learning architectures, alongside key microbiome data types such as amplicon sequencing, metagenomics, and functional gene profiling, with integration of environmental, agronomic, and meteorological datasets. Results: The prediction of crop productivity, disease risk, nutrient cycling dynamics, and soil health indicators may be enhanced by AI-assisted integration of microbiome, soil physicochemical, and meteorological data, according to several studies. However, broad generalizations about predictive robustness and generalizability are limited by significant diversity in datasets, validation methods, and model architectures. Discussion: To address these limitations, a five-phase implementation framework integrating centralized data systems, AI-driven analytics, multi-omics profiling, standardized soil sampling, and feedback-based model retraining within precision agriculture systems is proposed, providing a pathway for translating microbiome insights into field-scale decision support. Conclusion: AI-enabled soil microbiome applications hold significant potential for sustainable agriculture, but future advancements will require large, multisite datasets, improved validation strategies, interpretable modeling approaches, and integration with digital agriculture technologies, highlighting both opportunities and practical constraints.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.