关键词: 玉米, 干旱, 颜色和纹理特征, Fisher判别, 曼哈顿距离

Abstract: Using color and texture features to discriminate drought degree of maize plants is a new way for monitoring and identifying maize drought damage. It is of great significance to establish a good statistical discriminant model to realize that new way. This study collected images of maize plants under 5 drought degrees of free, light, medium, severe and extreme drought stress during 3 growth and development periods: emergence-jointing, jointing-tasselling and tasselling-maturing; and extracted the color and texture feature data of maize plants from the images by MATLAB software. Then, Fisher discriminant function groups were acquired through Fisher step by step discriminant analysis of the feature data training set by SPSS software. The 9 single-view(frontal, top or lateral view) statistical discriminant models for drought maize plants in emergence-jointing, jointing-tasselling and tasselling-maturing 3 growth and development periods were established by combining Fisher discriminant function groups and Manhattan distance discriminant rule. The single-view statistical discriminant models for drought maize plants in each growth stage were jointly constructed as a 3D statistical discriminant model. There was no significant difference in average discrimination accuracy between training and testing of all the single-view discriminant models of 3 maize growth stages. The average discrimination accuracies at training and testing of frontal and lateral view statistic discriminant models were over 90%. The difference of discriminating the different drought degree of frontal and lateral view statistic discriminant models maize was small. The average discrimination accuracies of all top view statistical models at training and testing were less than 85%, and there were significant differences in discriminating different drought degrees of maize. The accuracies of 3-dimensional statistical discriminant models for drought maize plants of 3 growth stages at training and testing were all over 97%, and had little difference in discriminating different maize drought degree. Among the single-angle statistical discriminant models, the discriminant effects of lateral statistical discriminant models were proved to be the best, followed by frontal statistics discriminant models, and the discriminant effect of the top view discriminant model was relatively poorer. The discriminant effect of 3D statistical discriminant model for maize plants in every growth stage was better than that of single-angle statistical discriminant models.

Key words: maize, drought, color and texture features, Fisher discriminant, manhattan distance