Abstract: Rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia oleifera production. The quantity and single fruit weight of camellia fruits are crucial indicators representing the Camellia yield. Therefore, a highly efficient and accurate monitoring of the quantity and single fruit weight of Camellia fruits can contribute to saving labor, material, and financial resources, as well as timely decision-making. Unmanned aerial vehicle (UAV) remote sensing has presented a high spatial resolution, fast data acquisition, and simple operation in recent years. An optimal operation period can be selected to obtain high-resolution aerial images, and thereby to realize the crop yield estimation in a large scale using the fruit numbers. However, only a few kinds of research are focused on the estimation of Camellia yield using UAV images and fruit number identification. In this study, a rapid yield estimation of Camellia oleifera was realized via the automatic detection of canopy fruit using UAV images. Firstly, a DJI Mavic 2 PRO UAV platform and Hasselblad L1D-20C camera were utilized to obtain UAV aerial images in the study area. 120 camellia oleifera trees were selected by random sampling for close-up UAV shooting, and manual picking, and weighing. A Mask RCNN framework was then employed to automatically detect and count the canopy fruits in the sample trees using UAV close-up images. Finally, two common Linear Regression (LR) and K-Nearest Neighbor (KNN) were used to build the relationship between the predicted fruit numbers of tree canopy and the measured. A yield estimation model was thus constructed using automatic detection of canopy fruits, according to the total numbers of sample trees and the average weight of single fruit. The results showed that: (1) There was an excellent performance of crop yield estimation via the automatic detection of canopy fruits using close-range photography of UAV, compared with the traditional method. (2) UAV ultra-low-altitude close-up images combined with Mask RCNN network effectively detected the camellia oleifera fruits under different lighting conditions, with an average F1 value of 89.91%. (3) There was well consistency between the predicted fruit numbers of tree canopy identified by Mask RCNN network and the measured, with R2 higher than 0.871. (4) The yield estimation results showed that the combined LR/KNN models presented a higher accuracy of yield estimation with R2 and NRMSE ranging from 0.892 to 0.913, and 28.01% to 31.00%, respectively. Consequently, the rapid yield estimation of Camellia oleifera sample trees can be achieved using automatic detection of canopy fruits from UAV images. The finding can provide highly versatile and great potential for rapid yield estimation and intelligent monitoring of the crops or trees in large areas.