Early detection and evaluation method and system for abiotic stress tolerance of new forest germplasm

By collecting and comprehensively analyzing multi-dimensional indicators, and combining principal component analysis, grey relational analysis and clustering algorithms, an early detection and evaluation method for the abiotic stress tolerance of new forest tree germplasm was constructed. This method solves the problems of detection lag and difficulty in identifying stress types in existing technologies, and achieves efficient and accurate evaluation of forest tree stress resistance breeding.

CN122173785APending Publication Date: 2026-06-09NANTONG UNIV
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Patent Information

Application Number
CN202610402673.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-30
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for detecting abiotic stress in forest trees suffer from problems such as detection lag, low efficiency, and inability to perform high-throughput screening. Furthermore, single or a few fluorescence indicators are insufficient to accurately assess stress resistance characteristics and identify stress types. These technologies are also highly species-specific and lack a multi-dimensional comprehensive evaluation system.

Method used

We constructed an early detection and evaluation method for the abiotic stress tolerance of new forest tree germplasm by using multi-dimensional index collection, principal component analysis, grey relational analysis and clustering algorithm. Through comprehensive evaluation of chlorophyll fluorescence, phenotypic and physiological and biochemical indicators, we screened core indicators and their weights, constructed an abiotic stress assessment index using membership function, and used clustering algorithm to classify tolerance levels.

Benefits of technology

It enables comprehensive, objective, and efficient early detection of the abiotic stress tolerance of new forest tree germplasm, significantly improving the accuracy and efficiency of stress-resistant breeding work, overcoming the one-sidedness and instability of single-index evaluation, and enhancing the interpretability of the model and its ability to distinguish stress responses.

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Abstract

The invention discloses an early detection and evaluation method and system for abiotic stress tolerance of new forest germplasm, and the method comprises the following steps: collecting chlorophyll fluorescence indexes, phenotypic indexes and physiological and biochemical indexes of forest under abiotic stress conditions, and carrying out data cleaning standardization processing; performing principal component analysis on the multi-source standardized data set, and screening candidate indexes; performing grey correlation analysis by taking the comprehensive score of the stress tolerance of the forest tree as a reference sequence and the candidate indexes as a comparison sequence; determining a core index and a weight according to the correlation degree of the variance contribution rate of the principal component analysis and the grey correlation analysis; obtaining abiotic stress evaluation indexes from the core indexes by using a membership function; carrying out tolerance grade division on the forest germplasm by adopting a clustering algorithm based on the index; according to the method, early-stage, rapid and accurate evaluation of the abiotic stress tolerance of the forest germplasm is realized, and the stress resistance breeding work of the forest can be effectively guided.
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