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.