Study On Diagnosis Of Crops Disease Based On Image Recognition

Crop production is necessary resource for human's production and life and also a large proportion of our national product. Diseases and pests are important factors to restrict the growth of crops in agriculture producing, which may reduce yields of crops greatly and quality of products. Therefore, the research on identification of the type of crop pests and diseases has important practical significance and application value.At present, the diagnosis of crops diseases mostly depends on manual recognition, but some problems occur: on the one hand, it can be mistakenly diagnosed by farmers because they usually judge the symptom by their experiences; on the other hand, the disease treatment may be dallied over because the technician or expert can't go to locale to diagnose in good time. All these can be resolved through computer image processing and pattern recognition technology. So we hope to build an image recognition system to identify diseases and pests of crops. Pattern Recognition and Image Processing by way of computer software used to analyze diseases on leaf of crop in order to achieve the automatic diagnosis. Cucumber disease leaf was as an example in this paper and the major work is summarized below:1. Pre-processing for disease image of cucumberPre-processing on image of cucumber diseases, which includes clipping, channel selecting, smoothing, segmentation, contours extraction and spots extraction. Firstly, we moved complex background for image with the image clipping technology and select blue channel on which the spots displayed most clearly, and then wiped noises for the image with Median filter. Secondly, threshold method was used to separate spots, and the outcome is a binary image. Lastly, the spots contours were extracted to plus with the original image, and then, the spots were extracted.2. Feature-extraction for spots imageColor, texture and shape features of the image after pretreatment were extracted and stored in a text file with the format which in accordance with SVM model training. In this paper, 26 features which include 6 color features, 7 texture features and 10 shape features were extracted.3. Designing classifier with SVMGetting the diagnosis model of cucumber diseases by designing classifier with SVM method and training the stored features.4. System-realizationVisual C++ 6.0 and OpenCV were used to develop the cucumber disease recognition system CDRS1.0 which realized quick identification of cucumber downy mildew, brown spot and angular leaf spot based on image processing.