In order to obtain the shape parameters of growing fruit and monitor the fruit development status, an apple point index estimation method based on local point cloud was proposed. The method could estimate the shape index parameters such as volume, height and diameter of apple through apple local point cloud data. Firstly, the geometric model of apple was constructed by using the method of ellipsoidal surface equation, and the height, diameter and volume of apple geometric model were calculated. Kinect V2 was used to get point cloud data from any angle. Secondly, the passthrough filtering method was used to remove the background of point cloud data and the bounding box reduction algorithm was used to streamline the point cloud, and then the apple’s local point cloud was obtained. After that, the genetic algorithm was used to solve the optimal apple geometric model parameters. Finally, the height, diameter and volume of apple optimal matching model were used to estimate the shape index parameters of matching apple. The experiment collected local point cloud data of 250 apples at three different angles, namely the top, side and bottom of apple. Using this method, the shape indicators of 250 apples were estimated under these three angles. A linear regression method was used to analyze the linear correlation between the estimated value and the true value. The linear regression fit of each indicator was higher than 0.7. Among them, when the angle was the side of the apple, the linear regression fitting effect was the best, and the R2 was up to 0.948. And the average error of the apple volume estimation results under angles was no more than 16.16mL, the average error of the height estimation result was no more than 2.92mm, the average error of the diameter estimation result was no more than 2.35mm, and the average error was within the allowable error range. The experimental results showed that the method was stable and practical. 