Scholarlink

<p>Plant phenotyping is the science of quantitatively describing the observable characteristics of the plant. It is considered as a reproducing process where, diverting from the usual manual assessments to big data approaches by which morphological, physiological, and biochemical traits are assessed quantitatively at scale. It is a vital transformation for plant breeding programme, because modern accelerated phenotypic methods such as genomic selection require timely and accurate phenotypic data. Those older phenotyping techniques are now constrained by the labour-intensive methods and the inaccuracies that a human may introduce, especially when genetic gains must be realized under climate change and food security threats. Now, High-throughput phenotyping (HTP) methods, aided by new-generation sensors, remote sensing, imaging platforms (hyperspectral, thermal, 3D) and machine learning allows quick non-invasive and precise evaluation of key traits such as yield, disease resistance and tolerance to stresses across diverse environments. AI-based analytical procedures sort the phenotypic data processing, genotype-phenotype linkage, hence helping facilitate selection. This review highlights the evolution of HTP platforms at aerial and ground levels and their integration in building scalable data-driven breeding pipelines for sustainable agricultural progress.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

High throughput phenotyping techniques in accelerated plant breeding

  • 期刊论文

  • 发表日期：

    2026年2月23日

查看全文
  • Lakshmi Bhavani Lankada,
  • G. Prasanna,
  • Narkhede Gopal Wasudeo,
  • Konusothu Subhasri,
  • G. Harish Kumar,
  • Mehdi Rahimi

摘要

Plant phenotyping is the science of quantitatively describing the observable characteristics of the plant. It is considered as a reproducing process where, diverting from the usual manual assessments to big data approaches by which morphological, physiological, and biochemical traits are assessed quantitatively at scale. It is a vital transformation for plant breeding programme, because modern accelerated phenotypic methods such as genomic selection require timely and accurate phenotypic data. Those older phenotyping techniques are now constrained by the labour-intensive methods and the inaccuracies that a human may introduce, especially when genetic gains must be realized under climate change and food security threats. Now, High-throughput phenotyping (HTP) methods, aided by new-generation sensors, remote sensing, imaging platforms (hyperspectral, thermal, 3D) and machine learning allows quick non-invasive and precise evaluation of key traits such as yield, disease resistance and tolerance to stresses across diverse environments. AI-based analytical procedures sort the phenotypic data processing, genotype-phenotype linkage, hence helping facilitate selection. This review highlights the evolution of HTP platforms at aerial and ground levels and their integration in building scalable data-driven breeding pipelines for sustainable agricultural progress.