Target Recognition and Trajectory Planning of Apple Harvesting Robot considering Color Multimedia Image Segmentation Algorithm

Ya Wang

doi:10.1155/2021/2817869

Abstract

For the purpose of significantly reducing the processing time of the apple harvesting robot during the harvesting process, it is highly necessary to carry out the corresponding studies on the methods for rapid recognition and trajectory planning. Through the comprehensive application of information relevance, the image processing area can be reduced. For image recognition and trajectory planning, the related template matching algorithm for removing the mean value and normalization product can be adopted, and segmentation methods based on different threshold values can be used for the realization of the effect. Subsequently, the comparative experiments are properly carried out to verify the effectiveness of the method used.

Highlights

Advances in Multimedia a evident color difference in the apple fruit and the background
A set of pictures of an orchard is taken in a natural environment, focusing closely on the background and apple fruit area. e background mainly refers to the branches, the sky, the green leaves, and other areas where the apple fruit is located. e values of the color factors R, G, and B are subject to comprehensive statistical and other related analyses
The author has applied the OTSU method, which is often referred to as the segmentation method based on the dynamic threshold value

Summary

Ya Wang

E reason for selecting the image segmentation method based on color features is that there is Advances in Multimedia a evident color difference in the apple fruit and the background. We can determine the fixed threshold value and use the value obtained to identify the corresponding fixed threshold on the fruit image It is found through a large number of experiments that the segmentation method based on the fixed threshold value still has some defects. (1) For the images acquired, after we carry out certain processing by using the relevant methods, it is necessary to identify the harvesting target fruit effectively based on the principle that the fruit essence is the closest to the image center(xc, yc). (2) We segment the image into four areas based on a certain method, that is, areas A, B, C, and D, and

Branches Sky

Test Results and Analysis

Group number of the images