A method for identifying lawn grass comprising: capturing (300; 600) an image of terrain in front of a mower; segmenting said image into square neighborhoods; calculating (345, 350, 355, 360, 365) values for at least two image statistics for each of said neighborhoods; comparing (370; 615) said image statistic values of said neighborhoods to predetermined image statistic values for grass, characterised by generating a binary representation of each image statistic for each neighborhood, wherein said neighborhood for said image statistic is marked as grass-containing when said image statistic value of said neighborhood compares favorably to a predetermined image statistic value for grass, and said neighborhood for said image statistic is marked as not containing grass when said image statistic value of said neighborhood does not compare favorably to said predetermined image statistic value for grass; weighting (380; 625) each of said binary representations of each image statistic by multiplying said binary representations of said image statistics by predetermined normalized weighting coefficients; summing (385; 630) corresponding neighborhoods for all of said weighted binary representations of said image statistics; and applying (390; 635) a binary threshold to each of said summed neighborhoods, wherein said binary threshold produces a binary map representing grass-containing areas and non-grass-containing areas; wherein said image statistics include at least one of the following edge based texture statistics: mean grayscale horizontal intensity, mean grayscale vertical intensity, vertical binary variance, mean vertical binary edge response area, horizontal and vertical neighborhood centroid location, horizontal binary variance, or mean horizontal binary edge response area; said at least one edge based texture statistics are generated using a vertical texture image of said image and/or a horizontal texture image of said image capturing (300; 600) an image of terrain in front of a mower; segmenting said image into square neighborhoods; calculating (345, 350, 355, 360, 365) values for at least two image statistics for each of said neighborhoods; comparing (370; 615) said image statistic values of said neighborhoods to predetermined image statistic values for grass, characterised by generating a binary representation of each image statistic for each neighborhood, wherein said neighborhood for said image statistic is marked as grass-containing when said image statistic value of said neighborhood compares favorably to a predetermined image statistic value for grass, and said neighborhood for said image statistic is marked as not containing grass when said image statistic value of said neighborhood does not compare favorably to said predetermined image statistic value for grass; weighting (380; 625) each of said binary representations of each image statistic by multiplying said binary representations of said image statistics by predetermined normalized weighting coefficients; summing (385; 630) corresponding neighborhoods for all of said weighted binary representations of said image statistics; and applying (390; 635) a binary threshold to each of said summed neighborhoods, wherein said binary threshold produces a binary map representing grass-containing areas and non-grass-containing areas;