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Rice image segmentation is a key step to obtain rice growth parameters, and plays an important role in rice production. The performance of traditional rice canopy image segmentation algorithm largely depends on the quality of the training data set, and the segmentation result is easily affected by the variable light intensity in the field, which leads to the poor estimation accuracy of rice growth information. In order to solve the above problems, an unsupervised Bayesian method based on Lab color space was proposed for field UAV image segmentation. With the unsupervised learning approach, the model parameters were directly learned by using unlabeled data from each individual UAV image. Different images had different model parameters, and this made the algorithm adaptable to images taken under a wide variety of conditions. The proposed algorithm was applied to UAV image segmentation of rice field in late tillering stage, and compared with RGB-GMM, HSV-GMM and All-GMM algorithms. Applying the algorithm on diverse UAV images in 10m height achieved an average recall, precision and F1 score of 0.8427, 0.7570 and 0.7948, respectively. Applying the algorithm on diverse UAV images in 15m height achieved an average recall, precision and F1 score of 0.8756, 0.7133 and 0.7788, respectively. These numbers outperformed the RGB-GMM, HSV-GMM and All-GMM algorithms. The experimental result demonstrated that the proposed method can accurately identify rice pixels in UAV images taken under diverse conditions.