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Detecting and estimating size of apples during the early stages of growth iscrucial for predicting yield, pest management, and making informed decisionsrelated to crop-load management, harvest and post-harvest logistics, andmarketing. Traditional fruit size measurement methods are laborious andtime-consuming. This study employs the state-of-the-art YOLOv8 object detectionand instance segmentation algorithm in conjunction with geometric shape fittingtechniques on 3D point cloud data to accurately determine the size of immaturegreen apples (or fruitlet) in a commercial orchard environment. The methodologyutilized two RGB-D sensors: the Intel RealSense D435i and the Microsoft AzureKinect DK. Notably, the YOLOv8 instance segmentation models exhibitedproficiency in immature green apple detection, with the YOLOv8m-seg modelclinching the highest AP@0.5 and AP@0.75 scores of 0.94 and 0.91, respectively.Leveraging the ellipsoid fitting technique on images from the Azure Kinect, weobserved remarkable metrics, including an RMSE of 2.35, MAE of 1.66, MAPE of6.15, and an R-squared value of 0.9. Challenges such as partial occlusion,where YOLOv8 sometimes misinterpreted immature green apple clusters, wererecognized. In a comparison of 102 outdoor samples, the Microsoft Azure Kinectshowed better performance than the Intel Realsense D435i, as supported by theMAE data. This study emphasizes the combined effectiveness of shape-fittingmethods and 3D sensors in improving fruitlet sizing for agriculture.

Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques