Abstract: The recognition of rice seedling is one of the significant parts of autonomous guidance for rice transplanting. Considering the segmentation of seedlings and remainder based on machine vision system， a simple dichromatic reflection model was established in RGB color space， which represented that the seedling could be recognized by using its color feature. The values of R， G， B components of seedlings and remainder were obtained in Photoshop software respectively and analyzed statistically in order to get the relation between them. In order to simplify the computing process， the weight values of a and b were set as 05， ExG index and Otsu method （ExG+Otsu method） which could obtain the optimal threshold were combined to distinguish the seedlings and remainder well. The RGB method and previous ExG+Otsu method were carried out to compare their performance intuitively. Their comprehensive performance was evaluated with segmentation quality factor and time consuming. The results have proved that the latter for segmenting was more efficient， highly stable and timesaving.

Key words: rice seedling, ExG index, Otsu method, image segmentation, quality factor