  We propose a novel, canopy density estimation solution using a 3D ray cloud
representation for perennial horticultural crops at the field scale. To attain
high spatial and temporal fidelity in field conditions, we propose the
application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping)
to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The
AgScan3D data is processed through a Continuous-Time SLAM algorithm into a
globally registered 3D ray cloud. The global ray cloud is a canonical data
format (a digital twin) from which we can compare vineyard snapshots over
multiple times within a season and across seasons. Then, the vineyard rows are
automatically extracted from the ray cloud and a novel density calculation is
performed to estimate the maximum likelihood canopy densities of the vineyard.
This combination of digital twinning, together with the accurate extraction of
canopy structure information, allows entire vineyards to be analysed and
compared, across the growing season and from year to year. The proposed method
is evaluated both in simulation and field experiments. Field experiments were
performed at four sites, which varied in vineyard structure and vine
management, over two growing seasons and 64 data collection campaigns,
resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of
vines with a combined total of approximately 93,000 scanned vines. Our
experiments show canopy density repeatability of 3.8% (Relative RMSE) per
vineyard panel, for acquisition speeds of 5-6 km/h, and under half the standard
deviation in estimated densities when compared to an industry standard
gap-fraction based solution. The code and field datasets are available at
https://github.com/csiro-robotics/agscan3d.
