Cluster closure, defined as the progressive filling of gaps between the berries in a grape bunch, is a key trait in vineyard management, impacting disease risk. However, traditional visual scoring methods are labor-intensive, subjective, and lack temporal resolution. Existing datasets rarely support fine-grained berry-level analysis, limiting the development of robust deep learning models. In this work, we present ViViD-5k, a large-scale in-field Vineyard Vision Dataset containing 5,000 images with dense annotations, including over 648,000 berry centroids and cluster segmentation masks spanning 13 grape varieties. Building on this dataset, we introduce GrapeSAM, a two-stage visual pipeline that combines point-based berry localization with prompt-based segmentation using Segment Anything, followed by transformer-based cluster segmentation. The pipeline enables automated, in-field estimation of cluster closure with minimal supervision. Quantitative results demonstrate strong segmentation and counting accuracy across diverse conditions, while visualizations confirm robustness on both in-domain and out-of-domain samples. This work provides a scalable and objective alternative to manual compactness scoring and supports high-throughput grape phenotyping with enhanced spatial detail.
@article{arxiv.2605.24353,
title = {ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation},
author = {Xiangzhi Tong and Chengrui Zhang and Mac Flaherty and Andre Matteo Garcia and Dominic Gorman and Jonathan Jaramillo and Justine E. Vanden Heuvel and Yu Jiang},
journal= {arXiv preprint arXiv:2605.24353},
year = {2026}
}