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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Nikita Aggarwal,
  • Mukesh Rathore,
  • Farkhandah Jan,
  • Divya Sharma,
  • Sundeep Kumar,
  • Mahendar Thudi,
  • Abdulqader Jighly,
  • Rajeev K. Varshney,
  • Reyazul Rouf Mir

摘要

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.