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Research on Cucumber Downy Mildew Images Classification Based on Fuzzy Pattern Recognition

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Abstract:

Disease is the main factor to restrict the vegetable quality. The abuse of pesticides not only destroys the environment but also causes excessive pesticide residues in vegetables. Timely and accurate identification of diseases is helpful to achieve scientific and rational use of pesticides, improve the quality of vegetables, and protect the environment. In this paper, cucumber downy mildew is studied by adopting image preprocessing, feature extraction, parameter optimization and pattern recognition method. Firstly, the image samples are analyzed by the gray statistics, then color and geometry characteristics are extracted, and digitalized, and normalized. Using the genetic algorithm to optimize these parameters values after a large number of samples of training, finally a group of more accurate characteristic parameters values is obtained. The fuzzy pattern recognition method is used to classify the disease image. The result shows that identification accuracy rate is above 90%, which is much better than other methods.

Keywords: CUCUMBER DOWNY MILDEW; FUZZY PATTERN RECOGNITION; GENETIC ALGORITHM; IMAGE CLASSIFICATION

Document Type: Research Article

DOI: https://doi.org/10.1166/sl.2012.1881

Publication date: 2012-01-01

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