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 protected] and [email protected] 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).

翻译：在苹果生长早期阶段检测并估算其尺寸，对于产量预测、害虫防治、以及制定与产量负载管理、采收和采后物流、营销相关的科学决策至关重要。传统水果尺寸测量方法费时费力。本研究采用先进的YOLOv8目标检测与实例分割算法，结合三维点云数据的几何形状拟合技术，在商业果园环境中精确测定未成熟青苹果（幼果）的尺寸。研究方法使用了两种RGB-D传感器：Intel RealSense D435i与Microsoft Azure Kinect DK。值得注意的是，YOLOv8实例分割模型在未成熟青苹果检测中表现出色，其中YOLOv8m-seg模型在[email protected]和[email protected]指标上分别达到0.94和0.91的最高得分。采用基于Azure Kinect图像的椭球拟合技术，苹果幼果尺寸估算的均方根误差（RMSE）为2.35毫米，平均绝对误差（MAE）为1.66毫米，平均绝对百分比误差（MAPE）为6.15毫米，决定系数（R²）为0.9。基于YOLOv8的分割技术在精确勾勒青苹果轮廓与测量尺寸时，部分遮挡（尤其在果实簇生情况下）会造成一定误差。在与102个室外样本的对比中，Microsoft Azure Kinect采集图像的尺寸估算法表现优于Intel Realsense D435i。这一优势通过指标数据得以体现：RMSE值（Azure Kinect 2.35毫米 vs Realsense D435i 9.65毫米）、MAE值（Azure Kinect 1.66毫米 vs Realsense D435i 7.8毫米）以及R²值（Azure Kinect 0.9 vs Realsense D435i 0.77）。