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

Abstract

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.