Automated extraction of Camellia oleifera crown using unmanned aerial vehicle visible images and the ResU-Net deep learning model - Ji, Yu; Yan, Enping; Yin, Xianming; Song, Yabin; Wei, Wei; Mo, Dengkui (Frontiers in Plant Science, 2022)
As one of the four most important woody oil-tree in the world, Camellia Oleifera has significant economic value. Rapid and accurate acquisition of Camellia Oleifera tree crown information is essential in improving Camellia Oleifera tree management efficiency and accurately predicting fruit yield. This study is the first of its kind to explore training the ResU-Net model with UAV images containing elevation information for automatically detecting tree crowns and estimating crown width (CW) and crown projection area (CPA) to rapidly extract tree-crown information. A Phantom 4 RTK UAV was utilized to acquire high-resolution images of the research site. Using UAV imagery, the tree crown was manually delineated. ResU-Net model’s training dataset was compiled using six distinct band combinations of UAV imagery containing elevation information (RGB, RGB-CHM, RGB-DSM, EXG, EXG-CHM, and EXG-DSM). As a test set, images with UAV-based CW and CPA reference values were used to assess model performance. With the RGB-CHM combination, ResU-Net achieved superior performance. Individual tree-crown detection was remarkably accurate (Precision=88.73%, Recall=80.43%, F1score=84.68%). The estimated CW (R²=0.9366, RMSE=0.1263m, rRMSE=6.44%) and CPA (R²=0.9559, RMSE=0.2986m², rRMSE=11.21%) values were highly correlated with the UAV-based reference values. The results demonstrate that the input image containing a CHM achieves more accurate crown delineation than an image containing a DSM. The accuracy and efficacy of ResU-Net in extracting Camellia Oleifera tree crown information have great potential for application in non-wood forests precision management.