Next-Generation Strategies for Developing and Commercializing Rust-Resistant Wheat through High-Throughput Phenotyping and Genomic Innovations

Authors

  • Muhammad Zulkiffal Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Aneela Ahsan Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Javed Ahmed Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Saira Mehboob Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Sadia Ajmal Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Faisal Hafeez Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Muhammad Ilyas Khokhar Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Muhammad Umer Farooq Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Majid Nadeem Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author
  • Muhammad Abdullah Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan Author

DOI:

https://doi.org/10.55627/pbiotech.003.04.1709

Keywords:

Artificial intelligence, Computer Vision, Multi-omics, Machine Learning, Plant stress, Precision agriculture, Rust resistance, Smart agriculture

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.

Author Biography

  • Aneela Ahsan, Ayub Agricultural Research Institute, Jhang Road, Faisalabad, Pakistan

    Principal Scientist (wheat)

    Wheat Research Institute, AARI, Faisalabad, Pakistan

htp

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Published

2025-12-28

How to Cite

Zulkiffal, M., Ahsan, A., Ahmed, J., Mehboob, S., Ajmal, S., Hafeez, F., Khokhar, M. I., Farooq, M. U., Nadeem, M., & Abdullah, M. (2025). Next-Generation Strategies for Developing and Commercializing Rust-Resistant Wheat through High-Throughput Phenotyping and Genomic Innovations. Integrative Plant Biotechnology, 3(4), 371-384. https://doi.org/10.55627/pbiotech.003.04.1709

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