Disparity map computation of tree using stereo vision system and effects of canopy shapes and foliage density

Abstract

Monitoring the growth of trees, plants, and crops is an important work in precision agriculture. Tree canopy geometric characteristics are related to tree growth and productivity. Computer vision techniques can be used to map tree canopy volume, which is useful for planning management. This study investigates the potential of using stereo vision system for obtaining tree disparity map for the analysis of geometric attributes. Experiments were conducted to examine the effects of the canopy shapes and foliage density on the performance of stereo vision system in disparity map computation. Two canopy shapes (conic and ellipse) and three foliage density levels were evaluated using two algorithms (algorithm based on local methods (ABLM) and algorithm based on global methods (ABGM)) to match pair stereo images. The well-known Middlebury dataset was considered and the performance of algorithms was evaluated on that. The results showed that the ABGM studied algorithm succeeded in computation disparity map on both Middlebury and trees images because it aggregated matching cost from several directions. The tree canopy shapes and foliage density did not affect the results of algorithms. Also, noises were numerous and more dispersed in ABLM matching algorithm. It was observed that maximum disparity limits search space. Best results were obtained in real value. For smaller value of maximum disparity than that of real value, disparity map had missed disparities. Window size was affected on maps and noise level and best results were obtained when this parameter was set to 15. Smaller value obtained more detailed map but with noise and un-smooth. The algorithm was robust for real trees in natural conditions.