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Peer-reviewedCC BYTheoretical and Applied Genetics

Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies

  • Nikita Aggarwal1
  • Mukesh Rathore1
  • Farkhandah Jan1
  • Divya Sharma2
  • Sundeep Kumar2
  • Mahendar Thudi3
  • Abdulqader Jighly4
  • Rajeev Kumar Varshney5
  • Reyazul Rouf Mir6,7
7 affiliations
  1. 1Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology, Wadura Campus, Sopore, 193201, Kashmir, India
  2. 2Division of Genomics Resources, ICAR-National Bureau of Plant Genetics Resources, New Delhi, India
  3. 3College of Agriculture, Family Sciences and Technology, Fort Valley State University, Fort Valley, GA, USA
  4. 4AgriSapiens Pty Ltd, Melbourne, VIC, Australia
  5. 5WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, 6150, Australia
  6. 6Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology, Wadura Campus, Sopore, 193201, Kashmir, India. [email protected]
  7. 7WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, WA, 6150, Australia. [email protected]
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Abstract

Agriculture plays a crucial role in the development of countries whose economies rely heavily on food production. In the face of climate change and growing global population, plant breeders are challenged to adopt more efficient crop improvement strategies. The advances in artificial intelligence (AI), particularly in large-scale data integration, analysis, and pattern recognition, have revolutionized several scientific disciplines, including plant breeding. In this review, we provide a comprehensive survey of the potential of AI tools in plant breeding with four key objectives: (i) revolutionizing high-throughput phenotyping, (ii) exploring AI-driven breeding methodologies beyond traditional approaches, (iii) optimizing breeding pipelines through improved modelling of genotype × environment × management interactions, and (iv) highlighting the limitations of AI in plant breeding and future directions. Case studies published during the past two decades illustrate successful implementations of AI-powered phenotyping and breeding frameworks for major traits across diverse crop species. Furthermore, AI tools show great promise in refining crop traits at the molecular level by increasing the accuracy and precision of emerging fields including gene editing and genomic selection. We emphasize the importance of interdisciplinary collaboration to maximize the benefits of AI in plant breeding programs and to support the sustainable and food-secure future. This review bridges the gap between AI and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and computational models for advancing precision agriculture. It will serve as a valuable resource for future plant breeding, accelerating crop improvement from phenotyping to genomic selection and breeding decision support.

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Cite

BibTeXRIS
BibTeX
@article{aggarwal2026harnessing,
  title = {{Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies}},
  author = {Nikita Aggarwal and Mukesh Rathore and Farkhandah Jan and Divya Sharma and Sundeep Kumar and Mahendar Thudi and Abdulqader Jighly and Rajeev Kumar Varshney and Reyazul Rouf Mir},
  journal = {Theoretical and Applied Genetics},
  year = {2026},
  month = jun,
  doi = {10.1007/s00122-026-05293-8},
  url = {https://doi.org/10.1007/s00122-026-05293-8},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies
AU  - Nikita Aggarwal
AU  - Mukesh Rathore
AU  - Farkhandah Jan
AU  - Divya Sharma
AU  - Sundeep Kumar
AU  - Mahendar Thudi
AU  - Abdulqader Jighly
AU  - Rajeev Kumar Varshney
AU  - Reyazul Rouf Mir
PY  - 2026
DA  - 2026/06/23
JO  - Theoretical and Applied Genetics
DO  - 10.1007/s00122-026-05293-8
UR  - https://doi.org/10.1007/s00122-026-05293-8
AB  - Agriculture plays a crucial role in the development of countries whose economies rely heavily on food production. In the face of climate change and growing global population, plant breeders are challenged to adopt more efficient crop improvement strategies. The advances in artificial intelligence (AI), particularly in large-scale data integration, analysis, and pattern recognition, have revolutionized several scientific disciplines, including plant breeding. In this review, we provide a comprehensive survey of the potential of AI tools in plant breeding with four key objectives: (i) revolutionizing high-throughput phenotyping, (ii) exploring AI-driven breeding methodologies beyond traditional approaches, (iii) optimizing breeding pipelines through improved modelling of genotype × environment × management interactions, and (iv) highlighting the limitations of AI in plant breeding and future directions. Case studies published during the past two decades illustrate successful implementations of AI-powered phenotyping and breeding frameworks for major traits across diverse crop species. Furthermore, AI tools show great promise in refining crop traits at the molecular level by increasing the accuracy and precision of emerging fields including gene editing and genomic selection. We emphasize the importance of interdisciplinary collaboration to maximize the benefits of AI in plant breeding programs and to support the sustainable and food-secure future. This review bridges the gap between AI and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and computational models for advancing precision agriculture. It will serve as a valuable resource for future plant breeding, accelerating crop improvement from phenotyping to genomic selection and breeding decision support.
SN  - 0040-5752
SN  - 1432-2242
ER  -