Automated tree detection and density calculation using unmanned aerial vehicles

Growth monitoring in tree plantation farms plays a crucial role in taking necessary actions from early stages to improve harvesting efficiency. Unmanned aerial vehicles (UAVs) provide a fast and efficient way to acquire data from large farms with difficult access. We propose a novel end-to-end system composed of multiple stages to identify areas where seeds have not grown as expected in young tree farms. The system acquires data from UAV flights to generate georeferenced orthophoto images. Next, local binary patterns, distance transform, and watershed segmentation methods are applied on images to detect trees. Finally, tree density distributions are calculated in the granularity of approximately 90 m2 tiles. We assessed the system performance by comparing detection results against a ground truth set of over 50000 trees from 16 orthophoto images for two tree species. The median deviation was 2 trees per tile for both species, where the median tree count was 14 and 9 per tile, respectively.