Abstract
L.) undergo structural and physiological changes throughout the growing season, progressing through distinct phenological stages that require regular monitoring. This dataset consists of high-resolution point cloud data acquired with a stationary terrestrial laser scanner (TLS) to document grapevine development from early leaf development to dormancy. Georeferenced point clouds were generated from 15 TLS scans along two vineyard rows at nine phenological stages. The dataset also includes multispectral and RGB photogrammetric point clouds and orthorectified raster products from an unmanned aerial vehicle survey conducted before harvest. Ground-truth measurements leaf area index, grape production, and pruning wood biomass were collected for each monitored grapevine. As a result, the dataset provides multi-temporal TLS observations that support grapevine structural analysis and development, phenological monitoring, and can be used for the development of AI-based models for precision viticulture.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 1
Claim · model-written summary
The dataset provides georeferenced point clouds from 15 terrestrial laser scanner scans along two vineyard rows at nine phenological stages of grapevines.
VitisOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“Georeferenced point clouds were generated from 15 TLS scans along two vineyard rows at nine phenological stages”
t0“dataset of grapevines”