ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation

• URL: http://arxiv.org/abs/2605.24353v1
• Date: Sat, 23 May 2026 02:30:02 GMT
• Title: ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation
• Abstract summary: Cluster closure is a key trait in vineyard management, impacting disease risk.<n>Traditional visual scoring methods are labor-intensive, subjective, and lack temporal resolution.<n>We present ViViD-5k, a large-scale in-field Vineyard Vision dataset with over 648,000 berry centroids and cluster segmentation masks spanning 13 grape varieties.<n>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.
• Score: 7.764774369757902
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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