Abstract: Abstract: Plant fixed management is the trend of precise production management in orchards in the future, and the extraction of crown information from an individual fruit tree is the key to fixed plant management. However, due to the relatively low height of apple trees, severe crown crossover, and the spatial resolution of remote sensing data, it is a challenging task to extract crown information from an individual fruit tree using the Unmanned Aerial Vehicle (UAV) LiDAR technology. The research explored the possibility of using the Light Detection and Ranging (LiDAR) data collected by UAV to extract the crown information of an individual apple tree, detecting and measuring the crown area and crown diameter of an individual fruit tree. Besides, the sensitivity of spatial resolution to an individual tree crown detection and information extraction result was analyzed. The specific process included the use of the Inverse Distance Weight (IDW) interpolation to generate the Digital Elevation Model (DEM), the Digital Surface Model (DSM), and the Canopy Height Model (CHM); then, the local maximum filter algorithm and the Marked-Controlled Watered Segmentation (MCWS) were used to detect and extract crown of an individual fruit tree. The accuracy of the method was evaluated by comparing it with the number and position of trees, the outline of the crown, and crown area and diameter obtained by manual visual interpretation. And the sensitivity of spatial resolution (0.1, 0.2, 0.3, 0.4, and 0.5 m) to the detection and information extraction result of an individual tree crown was quantitatively analyzed by changing the resolution of the Canopy Height Model (CHM). The results showed that the method can realize the detection and information extraction of the crown of an individual fruit tree, to accurately extract the crown area and crown diameter. The F1-score representing the detection accuracy of fruit trees was 95.03%, the recall was 93.37%, and the precision was 96.75%; the accuracy rate of an individual crown extracted was 86.39%, the omission error was 11.52%, and the commission error was 5.24%. The linear fitting results of the extracted dataset and the referenced dataset of the crown area showed that the coefficient of determination, the root mean square error, and the normalized root mean square error was 0.81, 4.44 m2, and 20.56%, respectively; the linear fitting results of the extracted dataset and the referenced dataset of the crown diameter showed that the coefficient of determination, the root mean square error, and the normalized root mean square error was 0.85, 0.62 m, and 14.79%, respectively. Crown area and diameter were overestimated to varying degrees. Besides, the results of crown detection and information extraction of an individual fruit tree were also affected by the spatial resolution of the canopy height model. The increase in spatial resolution led to a decrease in recall and the increase of precision and resulted in an increase of omission error and the decrease of commission error. In this experiment, the optimum resolution of the canopy height model was 0.3 m. Therefore, a rule of thumb was proposed that when the spatial resolution of the canopy height model was close to 1/10 of the average crown diameter of all fruit trees, the accuracy was best. It could effectively detect the crown of an individual fruit tree and extract the outline of the crown, to accurately extract the crown information of an individual fruit tree.