Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2D cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson r2=0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R2=0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.

翻译：葡萄果穗结构与紧凑性是影响病害易感性、果实品质及产量的复杂性状。其评估方法包括视觉评分、人工测量和计算机视觉技术，其中计算机视觉方法最具扩展性。现有果穗图像处理技术大多依赖传统分割方法或需大量训练的机器学习模型，泛化能力有限。基于大规模图像数据集训练的新型基础模型——统一分割模型（Segment Anything Model, SAM）无需额外训练即可实现自动化目标分割。本研究证明，无需微调的SAM在二维果穗图像中单个浆果识别方面具有高精度。应用该模型，我们成功分割约3,500张果穗图像，生成超过15万个浆果掩膜，每个掩膜均关联其在果穗内的空间坐标。人工识别浆果数量与SAM预测结果呈高度相关性（Pearson r²=0.96）。尽管图像中可见浆果数量因视觉遮挡常低估实际果穗浆果数，但研究表明该偏差可通过线性回归模型校正（调整R²=0.87）。我们强调果穗成像角度对浆果计数与结构分析的显著影响，并提出利用浆果位置信息计算与果穗结构及紧凑性相关的复杂特征的不同方法。最后，探讨了SAM在葡萄园条件下与现有图像生成与处理管道集成的潜力。