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<p>Intelligent high-throughput crop phenotyping platforms have emerged as a&#xa0;pivotal technological enabler for precision breeding in modern agriculture. By implementing automated, multi-dimensional phenotypic data acquisition and intelligent analysis, these platforms provide essential data support for constructing comprehensive “Genotype-Phenotype-Environment” (G-P-E) interaction models. The integration of artificial intelligence, computer vision, and agricultural robotics has significantly enhanced platform performance in data throughput, phenotyping accuracy, and adaptability to complex environments. Through systematic literature review and case studies, this research comprehensively analyzes recent advances and technical challenges in high-throughput crop phenotyping platform (HTCPP) from three perspectives: platform architecture design, data analysis algorithms, and intelligent application of phenotypic parameters. Future research directions and trends are also discussed to guide the intelligent transformation of crop phenotyping platforms.</p>

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Research Progress and Prospect of Intelligent High-Throughput Crop Phenotyping Platform

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Fei Liu,
  • Shudong Wang,
  • Longgang Zhao

摘要

Intelligent high-throughput crop phenotyping platforms have emerged as a pivotal technological enabler for precision breeding in modern agriculture. By implementing automated, multi-dimensional phenotypic data acquisition and intelligent analysis, these platforms provide essential data support for constructing comprehensive “Genotype-Phenotype-Environment” (G-P-E) interaction models. The integration of artificial intelligence, computer vision, and agricultural robotics has significantly enhanced platform performance in data throughput, phenotyping accuracy, and adaptability to complex environments. Through systematic literature review and case studies, this research comprehensively analyzes recent advances and technical challenges in high-throughput crop phenotyping platform (HTCPP) from three perspectives: platform architecture design, data analysis algorithms, and intelligent application of phenotypic parameters. Future research directions and trends are also discussed to guide the intelligent transformation of crop phenotyping platforms.