Study On The Grade Of Grape Leaf Disease In Complex Environment

China has the second largest grape planting area in the world,and the identification and prevention of grape diseases rely more on the experience of farmers and experts.Through experience and manual identification,the disease grade of grapes is divided,and the future development of diseases is predicted.This method relies more on empiricism,and the prediction of vineyards is not comprehensive and accurate enough.The collection of grape leaves mostly comes from the shooting under natural conditions.The light,shooting angle and blade stacking all have an important impact on the experimental results.This paper labels the grape leaf data and uses Unet++model to segment the grape leaves,Grape leaf disease has all the characteristics of typical plant leaf disease,and is an important tool for analyzing plant leaf segmentation,disease spot segmentation and disease grading under complex background.At present,traditional segmentation methods are mostly used to segment plant leaves in the field of computer vision.These segmentation methods typically include k-means clustering algorithm,OTSU threshold segmentation algorithm,and edge detection algorithm.However,these algorithms are difficult to get rid of the empiricism of manual intervention,but they still have advantages in some specific processing.In recent years,with the extensive application of depth learning in computer vision,the method of depth learning is expected to become an important way to automatically segment plant leaves.In this paper,how to segment grape leaves in a complex background was studied experimentally,and how to accurately grade grape leaf disease.Based on the structure of depth convolution neural network U-net,an improved hop neural network Unet++model is used,and the encoder uses Resnet18 residual network for feature extraction.After solving the problem of grape leaf segmentation from the complex background,this paper adopts OTSU Otsu method threshold segmentation,in which the EXG algorithm is applied to the binary image,the green vector is strengthened,the green part and disease spot of grape leaf are extracted,the usual grading system of plant leaf disease based on disease spot of grape leaf is improved,and the disadvantage of large error in disease spot segmentation is solved.The paper pattern method is used to verify the method used in this paper,which proves that the method in this paper can effectively grade grape leaf diseases,and the laboratory data are compared and verified,and the results are true and effective.It can accurately grade grape leaf diseases.