Image Processing And Automatic Detection Of Leaf Spots Of Crop Black Rot

This study aims at the quick detection of leaf spots of crop black rot. Integrated the knowledge and technologies of computer vision, digital'image processing, and pattern recognition, the image processing and detection of leaf spots is come true. Main contents and results are shown as follows:The research advancements and achievements in the fields of farm produce disease using machine vision technique are reviewed. The superiority of leaf spots inspection method is put forward.A Machine vision system for leaf spots inspection has been set up. It included: Person Computer, frame grabber, industrial CCD color camera, scanner, etc.Several algorithms of image processing technique are studied, including elimination of image noise, image segmentation, image enhancement, etc. The method of image processing techniques can meet the demand of quick detection of leaf spots of crop black rot.The defected image are acquired and pre-processed. Image noise is eliminated using median filtering. The method has briefness and facility, strong ability of eliminating noise. Elimination of illumination unevenness is based wavelet. In order to eliminate the influence of background, the images are studied. The distance is maximal between two-peak values in blue gray level histogram of the Nrgb color model. Images are segmented using the threshold of b gray level.In the aspect of images segmentation methods, a method of self-adaptive fuzzy threshold segmentation on Hue image was proposed according to the characters of disease image. A method about self-adaptive selection of window-width is given, which improves the correctness of threshold selection. The dissertation obtains a new histogram transformation method by combining two existing histogram transformation methods, which can improve efficiency of fuzzy threshold segmentation method. According to the character of leaf spots, interactive hand segmentation is also used.