Identification And Picking Path Planning Of Famous Green Tea Based On Vision

Recently,people need more famous green tea.However,due to the high-quality requirements of the high-quality tea raw materials,the picking efficiency is too low and the cost also is so high.How to improve the production efficiency of famous green tea at a lower cost becomes the problems that tea production enterprises need to solve urgently.With the development of artificial intelligence,automatic picking devices based on machine vision are used for picking famous green tea.This paper mainly studies the key technologies of famous green tea picking based on machine vision,including the identification and segmentation of famous tea,the location of famous tea picking points and the path planning of famous tea picking.Accordingly,this article mainly conducted the following research:1.A tea sprout recognition segmentation method based on an improved watershed algorithm is proposed in this study,which solved the problems of incomplete recognition and segmentation of tea sprout and mis-segmentation of old leaves.First,separate the R,G,and B channels of the tea photos taken from the tea garden,use the minimum error method to obtain the optimal threshold T’.For all pixels of the B component,the pixel values are set to be greater than the threshold of zero to generate the B’component,and then make the difference between the G component and the B’component.The operation generates the G-B’component.And the minimum error method is used to obtain the best adaptation thresholds T₁ and T₂ and enhance G-B’via piecewise linear transformation.Finally,the watershed function is used to complete the final division of the tea sprouts.100 different tea leaves samples were selected for experimental verification,and the average segmentation accuracy rate was 92.26%.2.This study proposed a method to obtain information on the picking point on the basis of the Shi-Tomasi algorithm.Images of tea sprouts in a tea garden were collected,and the G-B component of tea sprouts was segmented using the Otsu algorithm.The region of interest was set with the lowest point of its contour as the center.The characteristics of tea buds and branches in the area were extracted,and the Otsu algorithm was used for a second segmentation of tea sprout images.The tea buds were segmented using the improved Zhang algorithm.The branch feature binary image was used to refine the skeleton,and the Shi-Tomasi algorithm was used to detect the corners of the skeleton and calculate and mark the picking points of the shoots.1545 tea buds were selected for experimental verification,and the experimental results showed that the accuracy of using this method to identify the picking point was 86.43%.Then,based on this,the 3D coordinates corresponding to the two-dimensional coordinates of the picking point are obtained by using binocular vision principle.3.By comparing the ant colony algorithm with the empirical sequential method,the ant colony algorithm is used as the two-dimensional coordinate of the tea picking point for the advantages of the algorithm.First,through the control variable method,the various parameters in the ant colony algorithm are determined,and then the picking path planning experiment is carried out for the different distribution of the famous tea picking points.The experiment proves the effectiveness of the ant colony algorithm for path planning of the picking points.