Tongue fur is an important objective basis for clinical diagnosis and treatment in western medicine and tongue diagnosis for Chinese medicine. Given the high penetration and built-in sensors of smartphones, and the need for continuous monitoring of health conditions, we propose an automatic tongue diagnosis framework on smartphone. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, so we have to solve this problem to detect the correct tongue furs. In previous work mentioned that their tongue diagnosis systems are set up in a constrained well-controlled environment (e.g. with fixed lighting condition), but we purpose to let users make tongue diagnosis with their own smartphones no matter where they are. Therefore, we provide a way to detect tongue furs under different lighting conditions (e.g. fluorescent, halogen, and incandescent illuminant) by the combination of series methods: 1. Lighting condition estimation, 2. Tongue image color correction 3. Tongue fur (white fur) detection. We use the SVM to estimate the lighting condition and do the color correction with the corresponding correction matrix for current lighting condition. After getting the corrected tongue images, we use the detection model training by SVM to detect the white fur region in corrected tongue images. In this thesis, we propose a lighting condition estimation method according to color difference of tongue images taken with and without flash on the smartphone under different lighting condition; we also verify that it need to search corresponding parameter of correction matrix for color correction depend on different lighting condition; finally, we observe that the overlap rate of corrected tongue images for Hal. and Inc. lighting has been clearly upgraded with our correction parameter and the white fur can be identify if the overlap rate of corrected tongue images exceed 60%.