  Detecting and estimating size of apples during the early stages of growth is
crucial for predicting yield, pest management, and making informed decisions
related to crop-load management, harvest and post-harvest logistics, and
marketing. Traditional fruit size measurement methods are laborious and
timeconsuming. This study employs the state-of-the-art YOLOv8 object detection
and instance segmentation algorithm in conjunction with geometric shape fitting
techniques on 3D point cloud data to accurately determine the size of immature
green apples (or fruitlet) in a commercial orchard environment. The methodology
utilized two RGB-D sensors: Intel RealSense D435i and Microsoft Azure Kinect
DK. Notably, the YOLOv8 instance segmentation models exhibited proficiency in
immature green apple detection, with the YOLOv8m-seg model achieving the
highest<EMAIL_ADDRESS>and<EMAIL_ADDRESS>scores of 0.94 and 0.91, respectively. Using the
ellipsoid fitting technique on images from the Azure Kinect, we achieved an
RMSE of 2.35 mm, MAE of 1.66 mm, MAPE of 6.15 mm, and an R-squared value of 0.9
in estimating the size of apple fruitlets. Challenges such as partial occlusion
caused some error in accurately delineating and sizing green apples using the
YOLOv8-based segmentation technique, particularly in fruit clusters. In a
comparison with 102 outdoor samples, the size estimation technique performed
better on the images acquired with Microsoft Azure Kinect than the same with
Intel Realsense D435i. This superiority is evident from the metrics: the RMSE
values (2.35 mm for Azure Kinect vs. 9.65 mm for Realsense D435i), MAE values
(1.66 mm for Azure Kinect vs. 7.8 mm for Realsense D435i), and the R-squared
values (0.9 for Azure Kinect vs. 0.77 for Realsense D435i).
