  Measuring semantic traits for phenotyping is an essential but labor-intensive
activity in horticulture. Researchers often rely on manual measurements which
may not be accurate for tasks such as measuring tree volume. To improve the
accuracy of such measurements and to automate the process, we consider the
problem of building coherent three dimensional (3D) reconstructions of orchard
rows. Even though 3D reconstructions of side views can be obtained using
standard mapping techniques, merging the two side-views is difficult due to the
lack of overlap between the two partial reconstructions. Our first main
contribution in this paper is a novel method that utilizes global features and
semantic information to obtain an initial solution aligning the two sides. Our
mapping approach then refines the 3D model of the entire tree row by
integrating semantic information common to both sides, and extracted using our
novel robust detection and fitting algorithms. Next, we present a vision system
to measure semantic traits from the optimized 3D model that is built from the
RGB or RGB-D data captured by only a camera. Specifically, we show how canopy
volume, trunk diameter, tree height and fruit count can be automatically
obtained in real orchard environments. The experiment results from multiple
datasets quantitatively demonstrate the high accuracy and robustness of our
method.
