Abstract:In order to increase the efficiency and robustness of automatic recognition of cucumber downy mildew disease, a disease recognition method was proposed in the fashion of visual saliency. Firstly, image sample of RGB color space was transformed into HSV color space, and a color correction method was performed on the sample image. Then the colorcorrected image was transformed from HSV color space back to RGB color space, and a linear combination of the R, G, B components was carefully chosen to generate visual saliency map of disease area on the leaf image. Finally, based on the visual saliency map, the disease area was extracted from the leaf area of original image. 50 samples for testing were acquired from warm houses in northern Beijing from September to October, 2015. Samples were taken by consumer grade digital cameras and mobilephones with camera module. In order to focus on the problem of disease recognition, original leaf images’ background were removed manually and uniformly fitted into 512 pixel by 512 pixel squares before experiments. Result of testing shows that this method can effectively extract disease area from color image with relatively high accuracy, the average of mis-classification rate is 6.98%, better than Kmeans(11.38%) and OTSU(15.98%); the average running time is 0.6614s, faster than K-means(1.4249s); the RMSE of running time is 0.0515s, robuster is better than K-means. Result also shows that CC(Color correction) method makes better results than original proposed disease recognition method proposed, mis-classification rate was decreased from 8.63%(Saliency method) to 6.98%(CC+Saliency method).
