## 1 Introduction
Neural network frameworks like You Only Look Once (YOLO) have effectively segmented 2D images with high precision [19; 20], enabling the removal of unwanted points from generated depth maps. Ultralytics' YOLOv8 (Ultlarlytics LLC, Los Angeles, CA, USA) marks a significant improvement in the YOLO series, surpassing previous versions in speed and accuracy (v7n\(\sim\)15 ms, v8n\(\sim\)5 ms) [20]. YOLOv8's core architecture, based on YOLOv7 ELAN design principles [21], features the innovative C2f module, which enhances gradient flow for robust performance [21]. It also incorporates spatial pyramid pooling fusion, crucial for extracting contextual data from various image scales, boosting the model's ability to generalize. Additionally, YOLOv8's neck design eliminates convolutional layers in the up-sampling process and replaces the C3 module with the C2f module for optimized efficiency [21]. YOLOv8 offers a comprehensive training framework supporting key functionalities like object detection and instance segmentation [22; 23]. Detector2 is an open-source library tailored for computer vision tasks, including object detection, segmentation, and pixel-wise predictions [24; 25]. Developed using Python and built on the PyTorch framework, it allows for the dynamic creation and debugging of neural networks. The library features a comprehensive model zoo with pre-trained models on datasets such as COCO, LVIS, and Cityscapes. Detectron2 supports multi-GPU training and mixed precision to accelerate processes. The architecture comprises several key components: the backbone, neck, and heads. The backbone extracts features from images, supporting architectures like ResNet, ResNeXt, and EfficientNet [26]. The neck, featuring components like the Feature Pyramid Network (FPN), processes these features for multi-scale object detection. The heads are tailored to specific tasks (R-CNN for object detection, Mask R-CNN for instance segmentation), predicting outputs such as bounding boxes and segmentation masks. This structured approach makes Detectron2 a powerful and flexible framework for complex visual recognition tasks. In instance segmentation, the ResNet 50 and 101 (R50 and R101) models using FPN achieved the best mAP scores within their model depths on the COCO dataset [26]. However, the ResNeXt 101 (X101) achieved the highest overall mAP score but had nearly twice the processing time of the ResNet-101 FPN model and more than twice the processing time of the ResNet-50 FPN model [26]. The objective of this research was to conduct a comparative analysis of the YOLOv8-seg and Detectron2 models to determine the most effective method for point cloud segmentation in the context of harvested wild blueberry totes. This comparison was crucial for understanding how different deep learning architectures perform in a specialized agricultural setting, especially one that involves complex and irregular natural objects. The models were assessed based on their accuracy, efficiency, and ability to handle nuances in the dataset, such as the differentiation between berries and the tote. The innovation of this study lies in applying 2D neural networks to segment 3D point clouds through depth map conversion in real time. The comparison of RGB-based segmentation and depth map segmentation will showcase the superior performance of this innovative method.