Research On Maize Leaf Disease Identification Based On Image Processing

The target of the research is to diagnize main maize leaf diseases by machine vision. To improve the actuality of uneven distribution of plant solution technical personnel, and disease expert system developed can't meet customer requirements. And it is necessary for further development of intelligent disease expert system as a basic reseach.The research took gray spot, curvular leaf spot, bipolaris maydis, rust, northen blight, brown spot as examples. The diseases pictures of different varieties and periods were taken in fields. According to the features of crop maize disease, image processing algorithms were applied to obtain object image. Then image features were extracted, and approximate features were chosen. Finally, pattern recognition technology was used to identify diseases automatic.The paper studied adopting image processing, characteristic extraction and pattern recognition to identify disease, and the desired results were achieved.1. The standards of disease image acquisition were established. Different imaging factors such as camera brand, focus distance, imge resolution and reference object were stuied to address the effects on the recognition of the disease images. Disease image collection method was regulated. And laid foundations for the standardization disease image collection framework.2. Optimum and universality method of image processing was determined according to features of maize disease image. Agorithms of neighborhood average and middle filter were used to eliminate noise; and algorithms of image sharpening and image contrast were used to enhance image; then methods of threshold segmentation based on hue, iteration binarization, image morphological operation and contour extraction were adopted for image segmentation.3. Multivariable characteristics such as texture, color and figure characteristics were extracted. Applying gray level co-cccurence matrix and box counting methods to computer entropy, energy, inertia, contrast, relativity, informationization measure, fractal dimension et al; color components based RGB, HIS, YCbCr, color moments were computed by utilization of color models of HSV; the algorithms of contour extraction and edge detection were applied to extract shape features, suah as girth, area, rotundity, figture factor, scatter index, equivalent circle radius, inscribed circle radius.4. In order to improve recognition efficiency correctness, Genetic algorithm is used to choose approximate features, and 9 optimum and effective image features including informationization measure, fractal dimension, color components b and Cb, color moment, girth, area, rotundity, figture factor were selected as recognition features from 29 primordial features.5. Pattern recognition methods of fisher discrimination analysis and bayes discrimination analysis were applied to recognize diseases. The results indicated that the recogniton precision of six kinds of maize disease recognition is higher than 85% by fisher discrimination analysis, and higher than 90% by bayes discrimination analysis.The paper provided essential theoretical evidence and technological basis for the futurer development of plant disease recognition system. And it improved computer image processing technology in agriculture engineering field.