Nonlinear Screening Indicators of Drought Resistance at Seedling Stage of Rice Based on Support Vector Machine

Screening indexes for drought resistance in crops is a puzzler characterized with a few samples, multiple indexes, and nonlinear. Rationality of linear regression model and indexes obtained by linear screening based on empirical risk minimization are controversal. On the contrary, support vector machine based on structural risk minimization has the advantages of nonlinear characteristics, fitting for a few samples, avoiding the over-fit, strong generalization ability, and high prediction precision. In this paper, setting the survival percentage under repeated drought condition as the target and support vector regression as the nonlinear screen tool, 6 integrated indexes including plant height, proline content, malondialdehyde content, leaf age, area of the first leaf under the central leaf and ascorbic acid were highlighted from 24 morphological and physiological indexes in 15 paddy rice cultivars. The results showed that support vector regression model with the 6 integrated indexes had a more distinct improvement in fitting and prediction precision than the linear reference models. Considering the simplicity of indexes measurement, the support vector regression model with only 6 morphological indexes including shoot dry weight, area of the second leaf under the central leaf, root shoot ratio, leaf age, leaf fresh weight, and area of the first leaf under the central leaf was also feasible. Furthermore, an explanatory system including the significance of regression model and the importance of single index was established based on support vector regression and F-test.

摘 要作物抗旱性指标筛选具小样本、多指标和非线性等特点, 传统的基于经验风险最小原则经线性筛选获得的综合指标及在此基础上建立的线性回归模型的合理性受到质疑; 基于结构风险最小原则的支持向量机具适于小样本、非线性、泛化推广能力优异等诸多优点, 但可解释性差。本文以15个水稻品种苗期反复干旱存活率为因变量, 从24个形态生理指标中经支持向量回归(SVR)非线性筛选得苗高、脯氨酸、丙二醛、叶龄、心叶下倒一叶面积、抗坏血酸等6个综合指标, 以此建立的SVR模型拟合精度与留一法预测精度均明显优于参比线性模型; 如考虑指标测量的简易性, 仅以地上部干重、心叶下倒二叶面积、根冠比、叶龄、叶鲜重、心叶下倒一叶面积等6个形态指标进行评估同样可行。为增强SVR的解释能力, 基于F测验对SVR模型建立了非线性回归显著性与单因子重要性显著性的测验方法。