Objective: Aiming to obtain the weight of fragrant pear quickly to provide a basis for developing the fragrant pear grading device. Methods: This method acquired RGB-D images of fragrant pear by Kinect camera and converted them into point cloud data. The point cloud data was pre-processed and interpolated to generate a fragrant pear model. Then calculated the size parameter of the fragrant pear model. Finally, using the fragrant pear's volume predicted its weight. Results: Experimental results showed that the average relative error of the volume was 2.8%. Then the volume of fragrant pears was calculated by the error-compensated measurement method of the body scale parameter, and its weight was predicted and compared with the actual weight. The experimental results showed that the average relative error of the predicted weight was 1.96%. Conclusion: The fragrant pear quality prediction method provides a reliable reference for developing fragrant pear grading equipment.