This study aimed to develop an automated detection and sizing method for immature green apples (fruitlets) in a commercial orchard environment using the YOLOv8 object detection model and 3D shape fitting techniques.
The key highlights and insights are:
The YOLOv8m-seg model achieved the highest AP@0.5 and AP@0.75 scores of 0.94 and 0.91, respectively, for immature green apple detection and segmentation.
The ellipsoid fitting technique on 3D point clouds from the Microsoft Azure Kinect camera 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.
Partial occlusion caused by leaves and branches, as well as low light or shadow conditions, resulted in some errors in accurately delineating and sizing green apples using the YOLOv8-based segmentation technique, particularly in fruit clusters.
The size estimation technique performed better on the images acquired with the Microsoft Azure Kinect camera compared to the Intel RealSense D435i camera, as evident from the lower RMSE, MAE, and higher R-squared values.
This study demonstrated the feasibility of accurately sizing immature green fruit in early growth stages using the combined 3D sensing and shape-fitting technique, which shows promise for improved precision agricultural operations such as optimal crop-load management in orchards.
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by Ranjan Sapko... klokken arxiv.org 04-03-2024
https://arxiv.org/pdf/2401.08629.pdfDypere Spørsmål