Precision farming requires tree-canopy information for better management. Stereo vision is the technique to create a 3D model, and it needs to be adequately setup to avoid extreme data processing and unreliable results. Features detection is very important. Different parameters affect features in images. Because 3D accuracy is necessary, this study focused to investigate the effects of various baselines of a stereo camera on the well-known combination of feature detectors and descriptors and optimization of a stereo-vision-system for obtaining 3D-model of tree-canopy. Also, the effects of different parameters were investigated in RGB and Y color spaces. These parameters were three levels of density, two shapes of canopy (conic and ellipse), image rectification and un-distortion, metering mode, exposure time and ISO speed. The results showed that the best system was stereo-system with baseline of 12 cm and the best combination was SURF-BRISK. Also, SURF-FREAK and SURF-SURF combinations were appropriate afterwards. The precision value was 1 for the SURF-BRISK combination in the system with the baseline of 12 cm. The parameters including image rectification, metering mode, exposure time and ISO speed were affected by combinations performance. Images must be rectified before the implementation of detector algorithms. Use of the pattern mode and same exposure times and ISO speeds for both pair images were better. The recall values were decreased for various exposure times and ISO speeds. The results of algorithms were not affected by the tree-canopy shapes and density. So results can be used successfully for trees with larger size and different shapes and densities.

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