Abstract: The leaves of the Camphora longepaniculata can be used to extract essential oils, which possess various functions such as antimicrobial, insecticidal, anti-inflammatory, and antioxidant properties. These oils are widely applied in the pharmaceutical, food, and personal care industries. The accurate estimation of leaf weight is a key step for yield prediction and quality control. However, to date, there have been no studies on usin·g depth cameras to obtain 3D point cloud data for estimating camphor tree leaf weight. In this study, we proposed a method for accurately estimating the leaf weight of camphor trees in Yibin City using depth cameras and 3D point cloud technology. By deploying three depth cameras to capture 360-degree 3D point cloud images of individual trees, we extracted key factors, including tree height, canopy width, volume, convex hull surface area, and convex hull volume. These factors were then used to develop a leaf weight estimation model via a multiple linear regression algorithm. To address the issue of limited data, we applied a data augmentation strategy to expand the sample size and enhance the model's robustness. Ten-fold cross-validation results showed that the model achieved an R² of 92.77%, with mean absolute error (MAE) and root mean square error (RMSE) values of 0.160kg and 0.207kg, respectively, demonstrating high prediction accuracy. We recommend that future research integrate multi-source sensor technologies and optimize 3D data processing to enable precise management and sustainable development throughout the Camphora longepaniculata's entire lifecycle.