Reliable　and　accurate　visual　detection　of　crop　rows　is　prerequisite　for　implementing　successful　autonomous　navigation　for　plant　protection　robots.　A　visual　navigation　path　detection　approach　based　on　random　sample　consensus　(RANSAC)　algorithm　was　proposed.　Firstly,　the　excess　green　(ExG)　method　and　the　maximum　variance　between　classes　were　used　to　figure　out　gross　target　regions.　Secondly,　morphological　operations　and　dynamic　area　threshold　filtering　strategy　were　employed　to　filter　out　the　interferences.　As　outlier　points　significantly　influenced　the　estimation　accuracy,　RANSAC　algorithm　was　proposed　to　purify　the　inlier　point　sets.　Finally,　crop　rows　line　features　were　modelled　by　least　mean　square　techniques,　which　offered　a　degree　of　robustness　in　constructing　global　co-linear　features　in　contrast　to　Hough　transformation.　To　sufficiently　verify　the　effectiveness　of　the　idea,　wheat,　peanut,　corn　and　film　covered　potato　seedling　images　were　used　for　evaluation.　As　revealed　by　experimental　results　that　the　proposed　method　outperformed　Hough　transformation　in　the　crop　rows　center　line　extraction,　and　RANSAC　algorithm　rendered　the　method　more　robust　with　respect　to　noise　and　outliers,　which　allowed　the　successful　detection　rate　of　the　work　to　be　improved　by　18.8　percentage　points　and　arrived　at　93.8%.　The　overall　framework　made　sense　to　reliable　visual　navigation　for　plant　protection　robots.　©　2020,　Chinese　Society　of　Agricultural　Machinery.　All　right　reserved.