One method of creating a 3D model of a tree is stereo vision technique. Tree geometric characteristics have been used to predict yield, fertilizer application in citrus crops, water consumption or biomass. So, a disparity map and 3D model of the tree can be useful. There are different algorithms to match the stereo pair for calculating disparity maps. Many parameters affect the disparity map, such as the lighting that affects camera and imaging parameters, the calibration that needed to rectify the image, etc. In this study, the block-based stereo and semi-global algorithms with 5 implementations were compared to calculate tree disparity maps. Also, the effects of ISO speed, exposure time, metering mode (imaging parameters) and image un-distortion and rectification (pre-processing) were investigated. The results showed that the semi-global algorithm was better than the block-based stereo algorithm. Windows size affected on disparity maps and best results obtained when this parameter was set to default. Maximum disparity value limits search space. Best results obtained in real value. For smaller value than real value, disparity map was not calculated outside that maximum disparity. Therefore, disparities or pixels missed. It observed that image rectifying, ISO speed, exposure time and metering mode were affected on disparity map. When pictures did not rectify, disparity map calculated but with some errors. Camera parameters were affected on light intensity of pictures. Best disparity map obtained in conditions that ISO speed and exposure time had fewer value and metering mode was set to pattern mode and these parameters were similar for both left and right images.

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