Reserch And Implementation Of Intelligent Image Processing Algorithm On Maize Leaf Ill Spots

Using machine vision to solve the problem of crop illness diagnosis to realize the ultimate goal of crop illness prevention and saving the cost is one of the most significant characteristics of modern agriculture. Currently, lots of digital image processing techniques applied in this filed have been studied, one of the most important part is the segmentation algorithm which is vital to feature extraction and the accuracy of recognition. This paper mainly researched on how to integrate several classic segmentation algorithms together to segment 6 kinds of image ill spots on maize leaf which are Exserohilum Turcicum, Bipolaris maydis, Cercospora zeaemaydis Tehon and Daniels, Curvularia lunata Boed, Puccinia polysora, Physoderma maydis Miyabe. The system includes 4 parts which are image pre-process, image segmentation, feature extraction and classification and recognition. Based on system implementation need, the primary coverage of this paper are as follows:1.The study on image pre-process algorithm, mainly implement the function of image-resizing, automatic image-clipping and image enhancement. Due to large pixels of images shot from the maize field with Sony a700, direct process will cause low efficiency. So firstly, we reduce the image size and then clip out the interestd area using the automatic image clipping technique based on human vision which is implemented by computing 4 influence parameters of each blocks divided. To improve the accuracy, This paper add a color influence parameter which makes a better result. Finally, by using median filtering to erase the random noise of the image.2.The study on image segmentation algorithm.currently, most of the segmentaion algorithm usually use specific criterion to differentiate between ill spots and background. But in fact,due to the fuzziness and uncertainty of the crop leaf background and the ill spots, it's hard to use specific criterion to segment the illness spot out of the image. In order to improve the effect of the segmentation. This paper propose a self-adapting algorithm combining local threshold with regional growth. 3.The study of feature extraction and system verification. This paper mainly extract several morphological characters:Area, Girth, Circularity, Rectangular degree, Complexity. This part will use Fuzzy recognition algorithm to classify illness kinds and verify the influence of different illness spot. Image reducement parameter and the number of blocks divided from image can be defined through experiment.The system based on document-view architecture implement the function of image pre-process, image segmentation, feature extraction and automatic recognition, and all of the functions can be manipulated by clicking relevant menu. It's easy to process and have good user interface.