Design And Implementation Of Maize Disease Image Recognition System

Maize is one of the important food crops, a large acreage of maize in China, the distribution is very broad. Maize disease affect the output and quality of crop badly.The traditional crop disease diagnostic test for technical and manpower shortage, not only time consuming, and access to information lag also seriously affect the accuracy of disease diagnosis. This paper researched maize disease diagnosis by the image recognition technology , it show feasible and diagnostic accuracy by this study of maize leaf diseases. It provides a theoretical basis for the automatic identification and diagnosis of crop pests related research . This research work includes the following:(1) Maize production in several common diseases (maize leaf blight, gray leaf spot disease and maydis) as the research object in this paper. Natural light conditions, digital camera capture images of maize diseases, These images were processed for a unified operation by associated image processing software, in order to get diseased maize samples. Firstly, adaptive median filtering is performed to remove noises in images. This algorithm can filter noise, while maintaining the edge of clear objectives. Secondly, it was used by ultra-green feature and Ostu threshold segmentation method to segment images, Ultra-green features a unique image of plant characteristics, characteristics of first use of super-green image gray-scale transformation, re-use Ostu automatic threshold segmentation method for image segmentation by adjusting, to a good separation of the image part of the lesion. Finally, the image of the opening and closing operation to remove redundant spots; to eventually extract the lesion image. This paper analysis and research image pre-processing process. Through the noise filtering, image segmentation, image processing techniques such as mathematical morphology analysis, combined with the characteristics of maize diseases selected the image processing algorithms, The results show that the algorithms for image pre-processing operation are foundation of follow-up feature extraction and maize disease identification.(2) In the feature extraction part, combined with the characteristics of the image of maize diseases, this paper transform the image from RGB color space to HIS color space, As the image color distribution of the information concentrated in the low moments in which color moment, so this paper we use the color characteristics of the first, second and third moment to reflect the image of the color distribution. Color features were extracted from the image. This paper using gray level co-occurrence matrix to extract texture features. The algorithm has strong texture analysis and less computation.(3) In order to solve a small number of image samples of maize diseases , This paper proposed the support vector machine theory is applied to maize disease pattern recognition, the support vector machine (SVM) using the structural risk minimization principle, while taking into account training errors and generalization ability, it is to solve the small sample, nonlinear high-dimensional, local minimum problems has a unique advantage. This paper described in detail in the design of SVM classifier, and using a method based on fuzzy "one to many" SVM classifier, It solved the problem of a possible non-recognition zone. The results showed that: by vector machine (SVM) method ,recognition rate of the maize disease 83%, and identify categories of high precision maize diseases.(4) The basis of these studies, By using C # language and GDI + technology, Visual Studio.NET 2008 platform, developed a maize disease image recognition system. This paper described functions and modules for system design in detail.(5) Finally, the entire paper summarizes the major work done, and the future identification of maize disease research are put forward.In this paper, image recognition technology used in the identification of maize disease diagnosis, image recognition technology to expand the application scope of image recognition technologies for applications in agriculture provide a reference.