| As one of the most important method to realize information acquisition for agricultural robot, the machine vision technology could help recognize, locate and track targets to finish the servo control of the end-effector. For agricultural robot the machine vision system offen needs to work in two typical environments, the field environment and the orchard environment. Compare the different purpose of intra-row weeding robot for transplanted vegetables and the orchard yield estimation system based on machine vision, we could find that, the weeding robot needs to locate single vegetables quickly in images with a large color difference between the plants and2D field background. While the orchard yield estimation system could do images anylysis off line, but as the image background contains more3D objects and the color of green apple targets and background is so similar that the situation is more complex. A vision servo control system was studied to realize intra-row weeding without damaging crops, as well as an optimal camera pose search method to reduce the occlusion for the orchard yield estimation based on active vison servo technolog. Following is the main research contents:1. With a background segmentation way of G-R>Tr&G-B>Tb, a region-labeling-kind method based on2D histogram was presented to recognize and locate individual crops. It transformed the image searching to the histogram searching, which could reduce the number of individual areas and the searching time. The individual crops could be recognized indirectly by comparing the local region features of2D histogram. The experiment results showed that the algorithm time cost was16ms, with a correct recognition rate of97.34%on individual crops.2. As the crop space is large, a method of tracking navigation points between corresponding crop rows was studied to fit the navigation line based on Hough transformation. It showed a higher accuracy than the traditional navigation points tracking method.3. A vison servo control method of the intra-row weeding robot was presented to track the relative moving crops in the visual-field, which could realize the angle and rotating speed control of the crescent weeding hoe as well as the tracking of crops in the blind area of vison system. A vison servo control and human-computer interaction system was also designed to realize the vison servo control, the working stadus monitoring, the commond inputting and operation assisting information displaying.4. An image capturing system composed of two color cameras and an active flashing light was used to capture apple tree images at night. And a green apple recognition method was proposed. A hybrid classifier including an SVM method based on the advantage of H, S and normalized g and a Super-G method (2G-R-B) was developed to segement apple areas. To seperate the connected apple regions, the Euclidean distance transformation and a watershed method was used. The analysis of experimental results regarding64images showed that the average rate of correct recognition is89.30%.5. To reduce the occlusion area of the apple tree, the relationship between the visibility, the occlusion exploring ability and apple detection ability of different camera poses was studied. With the3D tree point cloud and the camera parameters from3D reconstruction of the image sequences, the occlusion map was generated based on the pinhole camera model. According to the anylysis of the different order of the existing camera pose sequence, the best visibility decision method could detect more apples after taking the same number of pictures.6. An active vision system was designed with the motion range on a semi-cylinder surface. According to the best visibility decision method, a searching strategy based on the Partical Swarm Opimization was presented to figure out the opitimal camera poses. The simulation experiments showed that this method could help search the opitimal camera poses. |
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