## 1 Introduction  
The fusion of computational methods with traditional farming practices in the cultivation of wild blueberries (_Vaccinium angustifolium_ Ait.) has led to increased efficiency and precision in cultivation [1, 2]. Wild blueberries (Figure 1) are a crop that presents distinct challenges due to their unique field conditions and harvesting requirements. Unlike cultivated (_Vaccinium corymbsum_ L.) varieties, wild blueberries grow without planting or seeding, often in rugged, uneven terrains [3, 4]. This non-uniform setting contributes to the complexity of their mechanical harvesting, making harvesting picker head implement height adjustments frequent. The use of advanced computer vision techniques, such as point cloud segmentation, explored in this research can address the unique challenges faced by wild blueberry harvester operators in ensuring consistent and full collection totes. Historically, 3D vision research in agriculture has predominantly overlooked specialty crops like wild blueberries [5, 6, 7, 8]. Harvesting wild blueberries is complex due to their varied sizes and shapes and natural clustering. These factors, combined with irregular field conditions, pose significant challenges for image processing and volume estimation. Conventional 3D vision approaches,such as RGB meshed segmentation or clustering-based segmentation [7; 9; 10], lack the depth and volume information crucial for accurately processing irregular and densely clustered objects like wild blueberries. In contrast, the segmentation of depth maps for 3D point clouds, as proposed in this research, offers a more comprehensive analysis by incorporating depth information, which is vital for precise volume estimation and spatial analysis. To optimize the point cloud volume estimation of harvested wild blueberries, segmenting outlier points is crucial for improving the accuracy of volume estimation algorithms [5; 11]. Accurate volume estimation is crucial for several reasons: it directly influences yield assessment, aids in quality control through the categorization of harvested berries based on volume (indicating maturity and quality) [12], and offers logistical benefits such as optimizing storage and transportation [13]. Subsequently, accurate volume estimation supports yield prediction and the generation of prescription maps, enhancing crop management through precision irrigation, fertilization, and drainage [14]. A critical step for accurate volume estimation is the effective segmentation of the tote containing the berries from the point cloud data. This involves distinguishing the berries from their storage tote and any other surrounding objects or noise within the 3D scan. This step is challenging due to the irregular and varying shape of the berries [15], variability in fill shapes, and other elements within the tote. Effective segmentation ensures that the volume computation applies only to the berries, excluding the tote and any extraneous elements. To use point cloud analytics for volume estimation, one study on the volume estimation of tree canopies using LiDAR highlighted three methods for volume estimation: convex hull, alpha shape, and voxel grid, finding the superior performance of convex hull and alpha shape over voxel grid [16]. Point cloud segmentation is crucial in enhancing the precision of volume estimation. Inaccurate segmentation could lead to the overestimation or underestimation of berry volume, causing inefficiencies in processing and potential economic losses [17; 18]. Moreover, precise volume estimation of harvested wild blueberries using 3D point cloud data has broader implications, paving the way for automated systems that streamline the harvesting process, reduce manual labor, and enhance accuracy and efficiency. This is particularly beneficial for crops like wild blueberries, where the harvesting and processing are labor-intensive and subject to human error. Figure 1: Example of wild blueberries (_Vaccinium angustifolium_ Ait.) at time of harvest, illustrating the irregular clustering.