Research On Recognition Disease Of Tomato Based On Computer Vision Technology

Internet of things of agriculture was based on the perception of the premise, has realized the people, the people and things, and the interaction of the perception. Key technology is the use of sensors to collect all kinds of status information, information to communicate via the Internet, communication network, etc, so as to realize the perception and understanding of foreign in the world. Through the optical sensor and computer vision technology, fast and efficient identification of crop disease have also become one of the important area of agricultural development of the Internet of things. In this article, based on computer vision technology, in view of the several kinds of common diseases of tomato leaf form, put forward some tomato leaf disease recognition features, these features extracting and calculating method is studied. On the basis of in-depth study of how to use these features to identify the disease related algorithm. Main work is as follows:Research in the structure and common diseases of tomato leaves, systematically analysis the structure of tomato leaf shape, leaf shape characteristic, the common tomato disease characteristics and the pathological characteristics were studied. In the synthesis of tomato leaf morphological changes and function is put forward on the basis of tomato early blight, late blight, leaf mildew and gray mold disease characteristics of several common are analyzed.In tomato leaf main characteristic research aspect, auxiliary light contrast algorithm can only get partial boundary, and ranging method of the operation is complicated, so the measurement points shoulds not be too much; If these two kinds of the algorithm, can maximum limit to reduce the number of distance measurement points at the same time for the complete leaf edge. Ranging method is proposed and the edge of the auxiliary optical contrast method determination algorithm, make the two complementary advantages determine the tomato leaf edge and tomato leaf characteristics conducted a series of research, make full use of tomato leaf edge feature and corner with veins branching point to determine the relation between leaf positions at all levels, proposed the main veins characteristic, secondary veins characteristic of approximate calculation method. Using fuzzy set description of tomato leaf surface and spot color color, put forward the HIS color model and RGB color model with difference of tomato leaf disease disease spot color features corresponding algorithm. Using color proportion distribution as the basic characteristics of the image. In order to reduce the amount of calculation, using the histogram statistics such as mean value, variance and variation coefficient to express color information. To plant disease image color model of the R, G, B and H, S, I carries on the analysis of data.In tomato disease recognition, USES the neural network optimization of tomato leaf surface and spot color color parameters of the fuzzy set, through the establishment of the veins partition, implements the disease spot and dystrophy spots to distinguish and separate recognition, effectively avoids the damage analysis of complicated pathology caused by a variety of disease spot analysis difficulty. On the basis of single disease spot recognition at the same time, combined with the feature of disease spot area to achieve the partitioning identification of complex diseases, the ideology on the simplification of the original diseases identification extension. Conducive to meet the demand of actual monitoring and production environments, at the same time, the study of more than for other plant foliar disease recognition diagnosis of diseases and other crops.Stage:in the simulation research on classification and recognition, this paper introduces the principle of BP neural network algorithm, and combined with the feature of texture, color, shape are the three kinds of diversified portfolio for training and testing respectively. Experiments show:using the BP neural network classification results for different kinds of diseases and concluded by nearest neighbor domain method show the same trend,2and1species combination difference is not big, but obviously with faster computing speed;3kinds of characteristics of the combination is the highest recognition rate, but the computing speed is lower than the second. In the realization of single disease spot image recognition, on the basis of many was to promote this method of disease spot image, makes the study of disease recognition ideas from simplification to diversification direction.Innovation point1:the veins of identification and introduction, different on color, texture, shape feature extraction, this paper will veins as a important feature for the extraction of effective work, Ye Maite diagnosis can identify the leaf curl disease harm such as vein disease.Innovation point2:the application of the veins partition, veins partition based disease spot distribution characteristics is beneficial to analysis whether because plants lack some nutrition and form spots, such as severe iron deficiency or zinc deficiency, etc., plant cell from the veins on the page with nutrients, if lack some nutrition, then the first part is far away from the veins of the position of the onset of symptoms, the disease spot first appeared in the central zone of the veins partition most likely.