Grapes Ready for Picking: Investigating the Readiness of Precision Viticulture Datasets for Scene Reconstruction

Public datasets of vineyard imagery were collected for detection, segmentation and disease mapping and are now proposed as a source for 3D scene reconstruction. Their documentation rarely states the properties that reconstruction depends on, such as view overlap, parallax and frame spacing, so it is unclear which can be reconstructed. This study measures that for the publicly available vineyard datasets. A structured search identifies eighteen datasets. Ten are characterized under a schema of thirty-six documented and measured properties, and only six contain overlapping views. Every sequence that registers is reconstructed with one fixed pipeline of structure from motion and 3D Gaussian splatting, giving twenty-seven reconstructions on two held-out splits. Registration is decided by the capture rather than by the dataset; novel-view quality varies as much within a dataset as between datasets; and frame spacing is the one capture property that limits reconstruction. Three preprocessing steps are tested as ablations on the failure cases. Band alignment restores registration, whereas dehazing a backlit pass and masking the chassis of a ground robot give no improvement that this study can resolve. The schema and pipeline apply to any vineyard dataset, and the results are published as an open online catalog.