Research On Image Recognition Methods Of Tea Bud

China is the hometown of tea.The Chinese people have been making and drinking tea since ancient times.In recent years,the domestic sales and export volume of tea in China have increased year by year,and the economic efficiency brought by tea has also increased rapidly.At present,the domestic tea picking method is mainly manual picking,mechanical picking as auxiliary.Manual picking is time-consuming,laborious and inefficient.Moreover,due to a large number of rural labor force pouring into cities for work,there is not enough labor force to carry out tea picking during tea picking period,which leads to some tea leaves missing the best picking period or even nobody picking them,causing economic losses to tea farmers.Although the existing mechanical picking is efficient,it is lack of selectivity and destructive.Therefore,it is necessary to research an efficient,selective and less destructive intelligent tea picking device to replace manual and mechanical picking.One of the most important work is to realize automatic and efficient identification of tea buds.In this study,tea buds were taken as the research object,a tea bud database was constructed,a tea bud segmentation method based on threshold value and support vector machine was explored,and a deep network learning model was used to identify tea buds,and its performance in tea bud recognition was experimentally verified.The main research contents of this paper are as follows:(1)A threshold method for tea bud segmentation was studied.In this paper,according to the color characteristics of tea buds,select the RGB color space and YIQ color space,the Lab color space and HSI color space and YCbCr color space do research analysis respectively,through the experiment found that R-B,I,B,S,Cb color component can be obvious highlight tea buds,so choose the five color components as the color of the threshold segmentation,combined with the gray histogram of each color component respectively using the OTSU method,fixed threshold method and iterative method of threshold segmentation.(2)Tea bud segmentation method based on support vector machine was studied.Through the analysis of linear classifier and support vector machine(SVM)in nonlinear classifier theory knowledge,in combination with tea bud color features and texture features of target and background region,R,G,B,H,S,I,L,a and B color component as color features of training segmentation model,energy,entropy,and the contrast as the training division texture feature of the model.Using linear kernel,polynomial kernel,RBF kernel and sigmoid kernel as kernel function of SVM,the results show that the segmentation model based on color feature,texture feature and RBF kernel is the best.(3)The method of tea bud identification based on deep network model is studied.According to the grade and quality requirements of tea,tea buds were divided into one bud and one leaf and one bud and two leaves.Because the growth posture of tea leaves was very different,the classification of occlusion was added into the identification model of tea buds.Chosen based on VGG-16,ResNet-50 and ResNet-101 the feature extraction of SSD depth of the network model and Faster R-CNN depth network model respectively for tea shoots data training samples,the experimental results show that the Faster r-cnn deep network model based on vgg-16 feature extraction network has better recognition effect.then choose VGG-19 feature extraction,network based on vector,IoU threshold parameter tuning.The mAP of tea bud recognition model is 85.67%.(4)The identification system of tea bud based on Windows platform is designed and implemented.Using PyCharm development software,using the Python language developed tea shoots recognition system,the system software has designed the four basic modules: data management module and deep learning model management module,task management module and the user management module.The system integrates the functions of importing image data,image enhancement,image annotation,model training,model testing and model saving,and the system realizes the rapid and automatic identification of tea buds.The research content of this paper realizes the automatic detection and classification of tea buds,which lays a foundation for the eventual large-scale automatic picking of tea leaves,and provides important theoretical value and technical support for the development of efficient and non-destructive tea picking robots.