Tree canopy geometric characteristics are directly related to tree growth and productivity, and this information has been used to predict yield, fertilizer application in citrus crops, water consumption or biomass. So a 3D model and a depth map of the tree can be useful. One method of creating 3D model is stereo vision technique. The comparison and performance assessment of different combinations of feature detectors and descriptors is very important in this technique. In this study, the performance of 12 combinations of well-known detectors and descriptors including BRISK with SURF, BRISK with BRISK, BRISK with FREAK, Harris with SURF, Harris with BRISK, Harris with FREAK, SURF with SURF, SURF with BRISK, SURF with FREAK, MSER with SURF, MSER with BRISK, and MSER with FREAK was evaluated. The included color spaces were HSV, H, YCbCr, Y, NTSC and RGB. The performance comparison for each combination was carried out in terms of precision and recall values using stereo image pairs of tree. It was observed that the largest number of keypoints were detected by MSER and SURF detectors almost in all possible spaces. Best combinations used from SURF detector and descriptor when the precision and recall results were considered. RGB and Y spaces were the best spaces to implement combinations. The combination of SURF with SURF, SURF with FREAK, and HARRIS-SURF were found to be preferable.

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