Navigation line extraction based on image processing for weeding robot

For weeding, compared to satellite navigation, using the location information of crops in the field to directly navigate the weeding robot is more accurate, so navigation line extraction is a key point in weeding robot autonomous navigation in the field. Though deep learning and neural network has developed fast in recent years, it does not run fast enough to be used in real time scenarios, so an image processing based navigation line extraction algorithm is proposed in this paper. We used a camera to take RGB images, of which the top and the bottom portion would be cut out and the middle portion would be remained to reduce the impact of distance between the crops and the robot. The green in the original image would be found out by transforming the image into the HSV color and then the morphological operation would be used to remove the noises for further image processing. Generally there are more than one ridges in the image, so it is necessary to find out the ridge in the middle for navigation. We clustered the crops according to the distance between them and chose the cluster in the middle to extract the navigation line. At last, the least square algorithm was used to calculate the navigation line. We used the slope of the navigation line and the distance between it and the center point of the image to control the direction of the robot. Experiments of the weeding robot on a simulated farm field showed that the algorithm can achieve the desired navigation effect and further experiments in the field also showed its good performance.