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.

精准农业需要树冠信息才能更好地管理。立体视觉是创建 3D 模型的技术，需要对其进行充分设置以避免极端的数据处理和不可靠的结果。特征检测非常重要。不同的参数会影响图像中的特征。由于 3D 精度是必要的，本研究的重点是研究立体相机的各种基线对众所周知的特征检测器和描述符组合的影响，以及优化立体视觉系统以获得树冠的 3D 模型。此外，研究了 RGB 和 Y 颜色空间中不同参数的影响。这些参数是三个级别的密度、两种形状的顶篷（圆锥形和椭圆形）、图像校正和不失真、测光模式、曝光时间和 ISO 感光度。结果表明，最佳系统是基线为 12 cm 的立体系统，最佳组合是 SURF-BRISK。此外，SURF-FREAK 和 SURF-SURF 组合在之后是合适的。系统中 SURF-BRISK 组合的精度值为 1，基线为 12 cm。图像校正、测光模式、曝光时间和 ISO 感光度等参数受组合性能的影响。在实施检测器算法之前，必须对图像进行校正。使用图案模式以及相同的曝光时间和 ISO 速度拍摄两对图像效果更好。对于不同的曝光时间和 ISO 速度，召回值有所降低。算法的结果不受树冠形状和密度的影响。因此，结果可以成功地用于具有较大尺寸和不同形状和密度的树木。