AI, multi-omics and phenotyping drive the next agricultural revolution

AI and deep learning models sit at the center of this revolution, integrating genomic, phenotypic, and environmental data into predictive systems that forecast performance under variable conditions. These models accelerate selection processes, optimize genotype-by-environment interactions, and help researchers understand complex trait architectures with remarkable accuracy.

Global food systems are under mounting pressure as climate change accelerates, reshaping weather patterns and amplifying the frequency of extreme events such as droughts, heatwaves, and floods. These challenges are not only threatening crop yields but also straining the resilience of agricultural ecosystems worldwide.

A new review, titled “Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges,” and published in Plants (2025), provides a comprehensive roadmap for harnessing artificial intelligence, multi-omics technologies, and high-throughput phenotyping to accelerate the development of climate-resilient crops.

Revolutionizing crop improvement with AI and multi-omics

The review highlights how integrating multi-omics approaches, genomics, transcriptomics, proteomics, metabolomics, and epigenomics, with artificial intelligence and machine learning is transforming the crop improvement landscape. By layering massive datasets, researchers can pinpoint the genes, regulatory pathways, and metabolites that confer resilience to environmental stresses such as drought, salinity, and temperature extremes.

Multi-omics enables a deeper understanding of the genetic and molecular drivers of crop stress tolerance. When combined with advanced computational models, this information allows breeders to identify key genetic markers and design targeted interventions faster than ever before. Cutting-edge techniques such as spatial and single-cell metabolomics, metabolite-based genome-wide association studies (mGWAS), and metabolite quantitative trait loci (mQTL) are also enabling unprecedented precision in discovering and leveraging adaptive traits.

AI and deep learning models sit at the center of this revolution, integrating genomic, phenotypic, and environmental data into predictive systems that forecast performance under variable conditions. These models accelerate selection processes, optimize genotype-by-environment interactions, and help researchers understand complex trait architectures with remarkable accuracy.

Advanced phenotyping and field-ready technologies

The study brings to light the critical role of advanced phenotyping technologies in driving progress toward climate-resilient agriculture. Traditional breeding programs have long been constrained by the labor-intensive and time-consuming nature of field evaluations. Now, high-throughput phenotyping (HTP) platforms equipped with RGB, multispectral, hyperspectral, LiDAR, and thermal imaging sensors are enabling non-destructive, rapid, and scalable measurements of critical plant traits across the entire crop cycle.

Field-ready phenotyping platforms, deployed via drones, ground vehicles, or stationary imaging systems, allow for real-time assessment of traits such as biomass, canopy temperature, photosynthetic efficiency, and early signs of disease or stress. These data streams, when processed by advanced machine learning algorithms, provide breeders with actionable insights to accelerate trait selection and improve prediction accuracy for yield and stress tolerance.

Studies reviewed in the paper demonstrate the value of integrating these systems. For example, deep learning models have been successfully applied to detect diseases in crops before visible symptoms emerge, enabling timely interventions that can save significant yield losses. Similarly, combining phenotypic imaging data with AI-driven analysis has shown strong predictive power for estimating yield and biomass under both optimal and stress conditions.

Microbiome synergies and societal impact

The review also sheds light on the potential of leveraging plant-associated microbiomes to enhance resilience. Beneficial soil microbes, bioinoculants, and synthetic microbial communities are being explored as complementary tools to improve nutrient uptake, mitigate stress effects, and enhance overall plant health. When paired with omics and phenotyping insights, microbiome management emerges as a key strategy for boosting crop productivity and stability under climate stress.

The societal and economic implications of these technological advances are far-reaching. By fostering the development of climate-resilient crops, the integration of AI, omics, and advanced phenotyping has the potential to safeguard livelihoods, stabilize food supply chains, and strengthen food security in vulnerable regions. These technologies also support sustainable farming practices by optimizing input use, reducing environmental impact, and promoting resource-efficient agriculture.

Additionally, the review notes the importance of workforce development and education. Building capacity for data-driven agriculture, through training programs in AI, bioinformatics, and phenotyping, is crucial for ensuring that farmers, researchers, and policymakers can fully leverage these innovations.

The review points to the critical need for continued development of open-access platforms and standardized data protocols to facilitate collaboration and data sharing. Such frameworks will be key to driving innovation and ensuring that the benefits of these technologies are accessible globally, particularly for smallholder farmers and developing economies disproportionately affected by climate change.