Unverified paper record
Next-Generation Strategies for Developing and Commercializing Rust-Resistant Wheat through High-Throughput Phenotyping and Genomic Innovations
Integrative plant biotechnology. · 28 Dec 2025 · 10.55627/pbiotech.003.04.1709
Abstract
Wheat rusts are the most important diseases leading to substantial yield losses. Early and precise detection of wheat rusts for early mitigation and disease control is imperative. This review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection. Additionally, various genomic interventions leading to the development of rust resistance in wheat are discussed in detail. High-throughput phenotyping (HTP) approaches enable early, non-destructive, and repeatable detection of wheat diseases. However, they need initial investment, expertise, and computational resources. RGB imaging achieves ~80% accuracy by capturing infected leaf coloration, while hyperspectral and fluorescence imaging can predict rust with over 90% accuracy, 3–8 days before visible symptoms. LiDAR, UAVs, and robotic platforms automate large-scale field phenotyping, and spectral indices (NDVI, PRI), thermal, and chlorophyll sensors detect early physiological changes. AI and machine learning models, including CNNs and SVMs, enhance diagnostic precision and reduce bias, while mobile apps, lateral flow devices, and IoT-based systems facilitate affordable, real-time rust detection and forecasting. Genomic interventions complement phenotyping, with marker-assisted selection (MAS) enabling precise tracing of rust resistance genes, and genomic selection (GS) allowing early multi-trait prediction. QTL mapping and GWAS identify major and minor resistance loci, while introgression from wild relatives and MAS reduce linkage drag and introduce novel alleles. Transgenic approaches, RNA interference (RNAi), and CRISPR/Cas9 gene editing enhance resistance through targeted gene modification, and gene pyramiding combines multiple loci for durable protection. Wheat pan-genome resources further support precise trait targeting, and speed breeding integrated with MAS, GS, or gene editing accelerates rust-resistant line development. Efficient seed system pathways ensure rapid dissemination, adoption, and resilience. The development and commercialization of rust-resistant wheat varieties under harsh climatic conditions are crucial for mitigating yield losses, reducing fungicide use, safeguarding farmer livelihoods, and ensuring sustainable food security.
Plant phenotyping relevance
コムギさび病の高スループット表現型解析手法を中心にレビューしており、画像・分光・熱・LiDAR・AIなどによる植物病徴の検出方法を扱うため。
abstractThis review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection.
abstractRGB imaging achieves ~80% accuracy by capturing infected leaf coloration, while hyperspectral and fluorescence imaging can predict rust with over 90% accuracy, 3–8 days before visible symptoms.
Code and data availability
This is a review article summarizing published HTP and genomic approaches for wheat rust resistance. No authors' phenotype datasets, images, sensor data, analysis code, models, or supplements with such assets are described. The only URL mentioned (CropX) is a commercial farm-management product cited as an example of an
No evidence-backed public reproduction asset is currently recorded.
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