Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U²-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U²-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U²-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was W_A = 0.0025D^1.943H^0.690 (R² = 0.912), the model for belowground biomass (BGB) was W_B = 0.012D^1.231H^0.525 (R² = 0.693), the model for CA-D was D = 4.31427C^0.513 (R² = 0.751), CA-H model was H = 226.51939C^0.268 (R² = 0.500). (2) The optimal AGB model for CA single-parameter was W_A = 1.80901C^1.181 (R² = 0.845), and the model for BGB was W_B = 1.25043C^0.772 (R² = 0.741). (3) The R² of Olea europaea L. biomass, as estimated by CA derived from the U²-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U²-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.

Carbon stock (CS) is an important indicator of the structure and function of forest ecosystems, and plays an important role in mitigating climate change, maintaining ecological system balance, promoting carbon trading, and other socioeconomic and ecological values. Olea europaea L. is a species of high economic and ecological value, and its excellent nutritional composition, strong drought tolerance, sustainable production characteristics, and promotion of agrodiversity make it important in guaranteeing food security. Accurately estimating the CS of Olea europaea L. offers a reliable reference for its artificial breeding and yield prediction. Firstly, an independent estimation model of Olea europaea L. CS was constructed, while a compatibility model of Olea europaea L. unitary and binary CS was constructed using nonlinear metric error. Secondly, in the CS compatibility model system, the total CS model of Olea europaea L. was constructed by the Logarithmic Nonlinear Seemingly Unrelated Regression (LNSUR) method with D and D²H as independent variables. The results show: (1) The independent model of Aboveground CS (AGCS) was C = 0.0014D^1.92876H^0.67174 (R² = 0.909), and the independent model of Belowground CS (BGCS) was C = 0.00723D^1.23578H^0.48553 (R² = 0.686). The AGCS compatibility model effectively addresses the issue of component sums not equaling the total, while maintaining a low RMSE (1.918); (2) The LNSUR model improved the accuracy of the BGCS model more significantly (R² = 0.787), and the estimated total CS also had a smaller RMSE (0.241~0.418); (3) Whole-plant CS of Olea europaea L. in 15 sample plots was estimated using the CS independent model and the LNSUR model with an R² of 0.964. This study is the first attempt to construct a CS estimation model for Olea europaea L., which provides a scientific and technological basis for the monitoring of its economic and ecological value indicators, such as yield and carbon sink capacity.