Segmentation algorithm based on color feature for green crop plants

Crop rows recognition is a principal issue in agricultural machinery vision system. In this paper, the color constant in RGB space was found to segment the green plant from the background by using statistical analysis based on the classical simple illumination model. For early green vegetation, the value of green component Gvalue is always greater than that of the other red Rvalue and blue Bvalue components (Gvalue>Rvalue and Gvalue >Bvalue, inferred to as RGB); and a relative segmenting error ratio was designed to evaluate the performances of the RGB presented in this paper and the Excess green + auto-threshold(ExG+atuo-threshold). In Experiment 1, the single factor variance analysis showed the algorithms (RGB and ExG+auto-threshold) had significant effect on the relative segmentation error ratio of the plant-soil images, and the corresponding segmented image using RGB were found that most of them could preserve morphological feature of plant compared with ExG+auto-threshold. And in Experiment 2, the double factor variance analysis showed that the algorithms, illuminant variations and their interaction had significant effect on the relative segmentation error ratio of the Canna images grasped consecutively, and the illuminant variations affected the threshold values of ExG+k-auto. The corresponding segmented images using RGB were found that most of them could delete the background noises compared with ExG+auto-threshold. And therefore, the RGB is a simple but efficient segmentation algorithm, and insensible to plant-soil and illuminant variations compared with ExG+auto-threshold.