Search Results

A heuristic line detection algorithm is described for computing guidance information from row crop images. The technique processes binary images representing crop rows against a soil background. Points along the centers of crop rows are enhanced using a modified run-length encoding procedure. The properties of lines in images can be improved by filtering based on characteristics of the object run-length. A clustering algorithm was used to aggregate pixels that fall on the same crop row. The technique was compared with the Hough transform, a common line detection technique in image processing. Both procedures accurately represented lines measured manually in a set of images representing a range of expected field conditions.

Image processing techniques were investigated for developing a guidance signal for a tractor operating on agricultural row crops. The guidance signal was computed from thresholded images segmented by a Bayes classifier. The distribution of crop canopy and soil background pixels in an image was approximated with a bimodal Gaussian distribution function. The parameters of the distribution were estimated by regression to systematically subsampled images. Run-length encoding was used to locate center points of row crop canopy blobs in the thresholded images. A heuristic line detection algorithm was used to determine the parameters defining crop row location on the image plane. Row parameters were used to compute a tractor guidance signal. Results are presented on the performance of the individual components of the algorithm.