Abstract
Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed us to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection in images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70% and 86.5% with inference times of around 89 ms for an input size of 1280 x 1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.

Abstract
Automated fruit harvesting systems rely heavily on accurate visual perception, particularly for crops such as the Arbutus tree (Arbutus unedo), which holds both ecological and economic significance. However, this species poses considerable challenges for computer vision due to its dense foliage and the morphological variability of its berries across different ripening stages. Despite its importance, the Arbutus tree remains under-explored in the context of precision agriculture and robotic harvesting. This study addresses that gap by evaluating a computer vision-based approach to detect and classify Arbutus berries into three ripeness categories: green, yellow-orange, and red. A significant contribution of this work is the release of two fully annotated open-access datasets, Arbutus Berry Detection Dataset and Arbutus Berry Ripeness Level Detection Dataset, developed through a structured manual labeling process. Additionally, we benchmarked four YOLO architectures - YOLOv8n, YOLOv9t, YOLOv10n, and YOLO11n - as well as the RT-DETR models, using these datasets. Among these, RT-DETR-L demonstrated the most consistent performance in terms of precision, recall, and generalization, outperforming the lighter YOLO models in both speed and accuracy. This highlights RT-DETR's strong potential for deployment in real-time automated harvesting systems, where robust detection and efficient inference are critical. © 2025 IEEE.