Study Of Recognition Method Of Crop Disease Based On Computer Vision

In order to ensure the effective application of pesticides over crop diseases control, agricultural producers must achieve the crop growth information accurately. Based on the acquired information, they can take rapid diagnoses on the causes and extent even the measures to control the diseases. With the rapid progress of computer processing and image recognition, much more fields of agriculture are using the technology to realize digital processing and automation. However, the data collection of crop growth is a hard job and the accuracy and real-time of information is always a concern in the field of agricultural production and scientific research. Therefore, it becomes so important to judge the type of crop diseases accurately by using computer technology to guide the agricultural production. Hence, this paper makes a study of corn and cucumber diseases recognition and diagnoses by different image processing and pathology based on computer vision technology.First, use illumination system and image procession device to collect diseases samples according to the requirements of infected leaves. But the devices used to be unsatisfactory in the collection. The noise always affects the quality of the image. Several common methods are used to remove the noise, but they also weaken the image on the brink, which is not useful in the image segmentation algorithms. In this paper, Winer filter and multi-scale recovery Retinex color image enhancement algorithms are used to improve the picture quality. And the facts prove that the image quality and effects are much more improved after the treatment.Second, study profoundly over image segmentation methods and the characteristics of all kinds of disease images. Cluster analyses are introduced in image segmentation to analyses and compare C-means clustering and Fuzzy C-means clustering segmentation algorithm characteristics. Experiments show that this method can reduce the computer cost and make segmentation time consuming.This article proposes extraction algorithm according to Kingsbury's idea of approximate displacement invariability and directionally selective Q-shift DT CWT transform theory based on his statistical and coefficient characteristics. The data of perimeter,area and shape over the infected leaves is collected to standardize it for further classification to ensure the accuracy. The trained samples withdrawn are put into the SVM trainer to have further feature recognition examination. Different methods are used and massive combined tests are carried on in different Video and SVM classification in the pretreatment and feature extraction stage. It shows that the method proposed in this article is not only effective in robustness but also significant in promoting recognition rate.To adopt 3-layer Bp neutral network to establish a crop disease diagnostic model and combine the annealing algorithm with the coarse grain parallel genetic algorithm can both keep the merit of synthesized genetic algorithm and speed up the searching time in genetic algorithm. After the optimization, the network diagnosis ability and the computing speed will be improved efficiently.To adopt VC+ programming software device can set up a system based on computer vision over crop disease recognition.This paper finishes a research over crop diseases recognition by using algorithm technology based on computer image processing, VC+ language, comprehensive computer vision device, artificial neutral network, wavelet transformation, support vector machines and statistical model recognition methods.