High-resolution UAV imagery-based instance segmentation for automatic counting of rubber trees across phenological seasons.

Accurate counting of rubber trees is important for yield estimation, refined field management, and the sustainable development of the rubber industry. In deep-learning-based counting, reliable canopy delineation is a prerequisite for accurate segmentation and subsequent statistical analysis. However, maintaining stable canopy segmentation under long-term and complex field conditions remains difficult, mainly because of overlapping crowns, interference from background vegetation, and pronounced seasonal changes. To address these issues, this study proposes CSAF, a rubber tree canopy segmentation model designed for high-precision canopy segmentation and counting from UAV imagery. CSAF consists of three main components: the Boundary Continuity Modelling Module (BCMM), the Physical Morphology Constraint Module (PMCM), and the Cross-Temporal Learning Adaptively Module (CTLAM). BCMM improves the representation of complex canopy boundaries by using the Fourier transform to suppress feature noise and state-space modelling to refine boundary responses. PMCM introduces a Poisson diffusion prior to constrain the segmentation results according to canopy morphology, thereby reducing the influence of irregular background vegetation. CTLAM adjusts the replay ratio and scale through a state-feedback mechanism, which helps mitigate inter-seasonal variation and improve cross-temporal generalization. In addition, this study presents RT-Set, a UAV-based rubber tree canopy dataset containing 5281 high-resolution images that cover the full growth cycle of rubber trees, from the budding stage to the defoliation stage. RT-Set provides a dedicated benchmark for precise rubber tree canopy segmentation and counting. On RT-Set, CSAF achieves an AP