Abstract: Taking tomato as the test material, the image data was obtained by using a visible light（RGB） camera system mounted on a scaffold under the facility environment, and the accuracy of different vegetation index algorithms for segmenting tomato canopy images was studied.And the evaluation of the method for extracting the canopy coverage of facility tomato was realized, in order to provide method guidance for the estimation of canopy coverage of other facility crops.The results showed that EXG algorithm, EXGR algorithm and CIVE algorithm could well be used to estimate the canopy coverage for facility tomatoes.The root mean square error（RMSE） between estimation value and true value for facility tomato canopy coverage was 0.049 for the EXG algorithm, 0.078 for the EXGR algorithm, and 0.088 for the CIVE algorithm.The coefficient of determination（R~2） was 0.911,0.845,0.841 for EXG algorithm, EXGR algorithm and CIVE algorithm respectively.The estimation results of canopy coverage for facility tomato were significant different among different vegetation index segmentation algorithms.Compared with the true value image, the EXGR algorithm had low segmentation accuracy, which overestimates the canopy coverage at 10 days after transplanting.Due to over-segmentation, the CIVE algorithm underestimated the canopy coverage at 66 days after transplanting.The EXG algorithm showed a higher segmentation accuracy at all growing stages and the estimated canopy coverage was close to the true value,compared to the other two algorithms.The EXG algorithm could achieve more efficient plant-soil segmentation and more accurate estimation for tomato canopy coverage than the other two algorithms,which provided methodological guidance of canopy coverage for other crops in facility.