Abstract:

【Objective】 The recognition and diagnosis methods of main maize leaf diseases using machine vision were studied in this paper. 【Method】 The diseases pictures of different varieties or periods were taken in fields, methods of threshold segmentation based on hue, iteration binarization, image morphological operation and contour extraction were adopted for image processing and image segmentation, then the texture, color and shape features were extracted. Genetic algorithm was used to get approximate features. Finally Fisher discrimination analysis was applied to recognize main maize leaf diseases. 【Result】 In the research, 28 characters including energy, informationization measure, fractal dimension, hue, cb, color moment, disease spot area, rotundity, figure factor, and others were extracted, and four approximate features were selected from 28 primordial features. The results indicated that the precision of the three kinds of maize disease recognition was higher than 90%. 【Conclusion】 Disease image obtained in fields were recognized by the application of digital image processing technology , analysis of image texture, color and figure characters, genetic algorithm and Fisher discrimination analysis. It has provided a technical support for the automatic recognition of crop diseases and insets with disease image obtained in fields.

Key words: maize, leaf disease, feature extraction, genetic algorithm, Fisher discrimination analysis