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<p>Abiotic stress tolerance is a critical trait in plant breeding programs aimed at developing climate-resilient crop varieties. The accurate identification and selection of stress-tolerant genotypes require comprehensive evaluation using multiple mathematical indices. However, the manual calculation of these indices from large-scale experimental datasets is time-consuming, error-prone, and computationally demanding. Here, we present PTSIonline (Plant Tolerance and Sensitivity Indices online, <a href="http://87.107.144.237">http://87.107.144.237</a>), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research. The platform implements 18 widely recognized stress evaluation indices including Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Superiority Index (SI), Abiotic Tolerance Index (ATI), Stress Susceptibility Percentage Index (SSPI), Relative Efficiency Index (REI), Modified Stress Tolerance Indices (K₁STI, K₂STI), Stress Distribution Index (SDI), Drought Index (DI), and Stress Non-Productivity Index (SNPI). PTSIonline features an intuitive user interface that accepts standard experimental data formats and generates comprehensive statistical outputs, visualizations, and genotype rankings within seconds. Comparative analysis demonstrates that PTSIonline provides the most extensive index coverage among available online tools while maintaining computational efficiency suitable for high-throughput phenotyping programs. The platform eliminates computational barriers in stress tolerance research, enabling researchers and plant breeders to rapidly identify superior genotypes from diverse germplasm collections. PTSIonline represents a significant advancement in computational tools for crop improvement under changing environmental conditions.</p>

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A comprehensive web-based platform for calculation of abiotic stress tolerance indices in plant breeding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Ahmad Jalili,
  • Hossein Sabouri,
  • Zohre Shoaei

摘要

Abiotic stress tolerance is a critical trait in plant breeding programs aimed at developing climate-resilient crop varieties. The accurate identification and selection of stress-tolerant genotypes require comprehensive evaluation using multiple mathematical indices. However, the manual calculation of these indices from large-scale experimental datasets is time-consuming, error-prone, and computationally demanding. Here, we present PTSIonline (Plant Tolerance and Sensitivity Indices online, http://87.107.144.237), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research. The platform implements 18 widely recognized stress evaluation indices including Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Superiority Index (SI), Abiotic Tolerance Index (ATI), Stress Susceptibility Percentage Index (SSPI), Relative Efficiency Index (REI), Modified Stress Tolerance Indices (K₁STI, K₂STI), Stress Distribution Index (SDI), Drought Index (DI), and Stress Non-Productivity Index (SNPI). PTSIonline features an intuitive user interface that accepts standard experimental data formats and generates comprehensive statistical outputs, visualizations, and genotype rankings within seconds. Comparative analysis demonstrates that PTSIonline provides the most extensive index coverage among available online tools while maintaining computational efficiency suitable for high-throughput phenotyping programs. The platform eliminates computational barriers in stress tolerance research, enabling researchers and plant breeders to rapidly identify superior genotypes from diverse germplasm collections. PTSIonline represents a significant advancement in computational tools for crop improvement under changing environmental conditions.