Rapid and precise monitoring of fruit development is essential for orchard management and fruit output forecasting. Compared to conventional technology, liDAR as an active monitoring technology offers a greater variety of nondestructive, high-precision measuring, and positioning options for fruits. On the basis of laser-point-cloud modeling tests on indoor potted apple trees, a three-dimensional spatial positioning and size detection approach suited for apple fruit trees was discovered, which gives vital scientific direction for monitoring apple growth. In this work, a technique for the spatial localization and identification of apple fruit size was developed using laser point cloud data collected from the ground. This article acquires data on apple trees using the Faro Fcous terrestrial laser scanner. The point cloud using the RANSAC approach is segmented, the segmented point cloud data is reconstructed, a threshold to classify is established, the fruit point cloud is identified, and the fruit′s spatial coordinates and radius are extracted. In comparison to the actual data, the root mean square errors of the horizontal distance, angle, height, and volume of the apple are 17.31 mm, 12.62°, 13.66 mm, and 3 512 mm3, the average absolute percentage errors are 22.94%, 13.63%, 5.19%, and 9.33%, and the coefficient of determination R2 is more than 0.90. This approach is applicable to the extraction of data for various round fruits and can rapidly and accurately find apple fruits and determine their volume.