植物在长期适应环境胁迫的过程中, 逐渐形成了与其生境相协调的形态、结构及生理等性状。研究表明可通过植物性状来评价植物的抗逆能力, 且多性状比单一性状评价更为准确。对植物耐受能力评价的方法多样, 各有优缺点, 目前尚没有研究对这些方法进行比较分析。本研究收集了国内外近13年的相关文献, 对目前常用的植物抗逆能力评价方法, 即平均隶属函数值、聚类分析、基于隶属函数和权重的模糊综合评价(简称模糊综合评价)、主成分分析、隶属函数及主成分分析复合评价法(简称复合评价法)、卷积神经网络、灰色关联分析的概念、原理、关键步骤以及优缺点进行了分析和比较。分析发现以上方法均采用多性状来评价植物抗逆能力, 其中, 平均隶属函数值、模糊综合评价和复合评价法基于模糊数学理论, 通过性状模糊化的方式建立模型; 平均隶属函数值、主成分分析、复合评价法、灰色关联分析能筛选出关键抗逆性状。主成分分析和聚类分析可以提供直观、易懂的数据展示方式, 有助于对抗逆能力评价结果的理解。以上方法可以在实践中相互补充, 针对不同的研究目的和数据特征选择合适的评价方法。

Aims: Plants are often exposed to environmental stresses. In order to survive, plants must adapt to these hostile environments by developing morphological, structural, and physiological traits that enable the plants to become compatible with their habitats. While there are many methods that would allow researchers can gain insight into the plants’ adaptive strategies, there has not a comparative analysis of these methods. To fill this gap in the literature, we collected articles from the thirteen years on seven methods that are commonly used to evaluate plant stress tolerance: (1) average membership function value, (2) cluster analysis, (3) fuzzy comprehensive evaluation (based on membership function and weights), (4) principal component analysis, (5) composite evaluation (combining membership function and principal component analysis), (6) convolutional neural network, and (7) grey relational analysis. Our objectives are to examine these methods’ main principles, key steps, advantages, and disadvantages. The overall goal is to select appropriate evaluation methods according to different research purposes and data characteristics, and to provide some theoretical basis for the accurate evaluation of plant resilience.

Review results: Our results indicated that fuzzy mathematics is an important theoretical foundation in the three methods(i.e., average membership function value, fuzzy comprehensive evaluation (based on membership function and weights), and composite evaluation (combining membership function and principal component analysis) are based on fuzzy mathematics theory). By using trait fuzzification, these methods enable researchers to establish models demonstrating how plant traits may affect plant resilience. We found that over half the models enable trait selection (i.e., average membership function value, principal component analysis, composite evaluation (combining membership function and principal component analysis), and grey relational analysis).

Conclusion: Average membership function value and gray relational analysis are often used for small sample data sets, and principal component analysis and convolutional neural network are often used for large sample data sets. Principal component analysis and cluster analysis can enable researchers to present their data in easily-interpretable visuals. Currently, the most commonly used method in the domestic biological field is the composite evaluation (combining membership function and principal component analysis). This literature review revealed that these seven methods have strengths that can be used to complement each other during evaluations of plant traits, allowing researchers to select evaluation methods that are tailored to specific research objectives and data characteristics.