AI-powered models revolutionize disease-resistant crop breeding for global food security

A new review sheds light on the revolutionary role of artificial intelligence (AI) in breeding disease-resistant crops, illustrating how machine learning, deep learning, and large-scale models are transforming plant pathology and genetics. Published in the International Journal of Molecular Sciences, the study titled “Artificial Intelligence-Assisted Breeding for Plant Disease Resistance” offers a comprehensive synthesis of the latest tools, algorithms, and multi-omics approaches that now define the frontier of agricultural AI.

By analyzing the recent trajectory of AI-driven crop improvement, from convolutional neural networks to large multimodal language models, the study provides a blueprint for how data-rich, predictive technologies are rapidly replacing manual field scouting and traditional breeding cycles. With plant diseases causing devastating yield losses worldwide, the research argues for a system-wide adoption of AI models capable of enhancing detection accuracy, shortening breeding cycles, and forecasting resistance traits across diverse crop genomes.

How is AI transforming disease detection in crops?

The study emphasizes that one of the most significant contributions of AI to plant science lies in rapid, image-based disease detection. Deep learning models, particularly convolutional neural networks (CNNs) and their real-time variant YOLO (You Only Look Once), now enable field-ready diagnostic tools capable of classifying disease symptoms on plant leaves and stems with precision. Through bibliometric analysis of over 340 articles published between 2020 and 2025, the study identifies CNN, YOLO, transfer learning, attention mechanisms, and vision transformers as the most dominant technologies driving progress in image-based plant disease recognition.

More advanced applications involve hybrid AI architectures like C-DenseNet, which integrates attention modules into CNNs for enhanced performance in detecting complex disease patterns such as wheat stripe rust. Novel lightweight models like MobileNet and EfficientNet are also featured for their role in powering low-computation disease detection tools usable on mobile devices and drones. These models excel in resource-constrained environments, ideal for real-time agricultural monitoring and deployment in remote farming regions.

However, while traditional CNNs process visual data in isolation, the study highlights a new generation of big models that combine image analysis with natural language processing. Integrations like YOLO-GPT and CTDUNet leverage GPT-4 and BERT alongside vision models to not only detect diseases but also generate text-based recommendations for farmers, improving decision-making at the field level. Platforms such as ITLMLP and BLIP-VQA are pushing the boundary further by enabling visual question answering systems tailored to agriculture.

How are genomic, phenomic, and multi-omics tools enhancing AI predictions?

Moving beyond surface-level diagnosis, AI is now being used to predict resistance traits deep within plant genomes. The study explores how genomic selection (GS), phenomic selection (PS), and multi-omics integration powered by AI models are reshaping breeding strategies.

In genomics, deep learning algorithms like support vector machines, attention networks, and GPTransformers are outperforming traditional linear models in predicting resistance to diseases like Fusarium head blight, stripe rust, and rice blast. These models can capture non-linear, epistatic interactions among genetic markers—critical for identifying disease resistance in complex polygenic traits. Hybrid methods such as dual-extraction modeling (DEM) and customizable architectures like CustOmics further enhance the predictive power by combining SNPs, transcriptomic data, and even microbiome profiles.

The phenomics layer adds another dimension, incorporating high-throughput imagery from UAVs and hyperspectral sensors to quantify traits like rust progression or leaf health. These datasets, when fed into machine learning frameworks, improve prediction accuracy for disease traits across different environmental conditions. PS is still a maturing field, but it holds promise for scaling breeding operations without the need for costly and time-consuming genotyping.

Multi-omics integration—incorporating data from transcriptomics, metabolomics, and environmental sensors—is another frontier. AI’s ability to harmonize these complex, high-dimensional datasets using early, late, or hybrid integration methods allows for holistic modeling of plant-pathogen interactions. Deep neural networks and transformer-based models have proven particularly adept at extracting latent patterns across modalities, making AI an indispensable tool in dissecting disease resistance at multiple biological levels.

What are the challenges and future directions for AI in breeding?

Despite the promise, the study acknowledges multiple roadblocks. One critical issue is the lack of high-quality, standardized datasets. AI models require vast volumes of annotated data for effective training, yet most crop-specific datasets are fragmented, proprietary, or inconsistent. Privacy and data sovereignty concerns further limit data sharing between institutions.

To address this, the authors advocate for federated learning (FL) as a key solution. FL allows decentralized training of AI models across multiple locations without centralizing raw data, protecting data privacy while improving robustness. Although still nascent in agricultural applications, initial trials using the PlantVillage dataset have shown that FL frameworks with CNNs like ResNet50 can achieve near-99.5% accuracy in plant disease detection.

The study also stresses the importance of explainable AI (XAI). Methods like SHAP and integrated gradients help users understand model outputs, increasing transparency and trust. These techniques are especially important for breeders and policymakers who rely on AI insights to make critical decisions.

Another innovation lies in merging FL with large language models (LLMs). The proposed smart breeding pipeline comprises four interconnected layers, multi-omics integration, federated learning, LLM processing, and decision-making, enabling predictive breeding strategies that balance data privacy, interpretability, and system scalability. Such a system could guide farmers in real-time while offering disease forecasts, trait recommendations, and intervention strategies, all through AI-powered interfaces.