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Journal article
Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies
Theoretical and applied genetics, Vol.139(7), 188
2026
PMID: 42337128
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.
Details
- Title
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies
- Authors/Creators
- Nikita Aggarwal - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirMukesh Rathore - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirFarkhandah Jan - Sher-e-Kashmir University of Agricultural Sciences and Technology of KashmirDivya Sharma - ICAR-National Bureau Of Plant Genetic ResourcesSundeep Kumar - ICAR-National Bureau Of Plant Genetic ResourcesMahendar Thudi - Fort Valley State UniversityAbdulqader Jighly - AgriBioRajeev K. Varshney - Murdoch UniversityReyazul Rouf Mir - Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir
- Publication Details
- Theoretical and applied genetics, Vol.139(7), 188
- Publisher
- Springer Berlin Heidelberg; NEW YORK
- Number of pages
- 42
- Grant note
- Murdoch University BT/AG/NETWORK/WHEAT/2019-20 / Department of Biotechnology, Ministry of Science and Technology, India (http://dx.doi.org/10.13039/501100001407)
- Identifiers
- 991005896257807891
- Copyright
- © Crown 2026
- Murdoch Affiliation
- Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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