UAV-based two-stage deep learning for tree crown segmentation and height estimation in Camellia oleifera plantations

Camellia oleifera is an important woody oil crop in southern China, and accurate extraction of crown and height attributes is essential for plantation management and yield assessment. However, joint extraction of these attributes and task-specific adaptation of deep learning models remain insufficiently explored. In this study, we proposed a UAV-based two-stage framework based on an improved U-Net++ architecture for crown segmentation and tree height estimation in C. oleifera plantations. Specifically, U-Net++ with an EfficientNet-B0 encoder was used in the segmentation stage to improve multiscale crown boundary delineation, while a U-Net++ regression model with a ResNet50 encoder and an scSE attention module was developed in the height estimation stage to enhance canopy-related feature learning. Different combinations of RGB imagery, multispectral bands, and elevation data were evaluated to assess the contribution of multisource data fusion. The results showed that the Multiband-CHM combination achieved the best crown segmentation performance, with an F1 score of 0.8719 and an IoU of 0.7807, whereas the Multiband-DSM combination yielded the best tree height prediction accuracy, with an R 2 of 0.7996 and an rRMSE of 27.06%. Furthermore, the framework demonstrated stable performance across regions with different planting densities and under large-area plantation conditions. These findings validate the framework for effective crown and height extraction, supporting precision management and yield assessment of large-scale C. oleifera plantations.

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