  Machine vision for plant phenotyping is an emerging research area for
producing high throughput in agriculture and crop science applications. Since
2D based approaches have their inherent limitations, 3D plant analysis is
becoming state of the art for current phenotyping technologies. We present an
automated system for analyzing plant growth in indoor conditions. A gantry
robot system is used to perform scanning tasks in an automated manner
throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's
payload captures the surface point cloud data of the plant from multiple views.
The plant is monitored from the vegetative to reproductive stages in light/dark
cycles inside a controllable growth chamber. An efficient 3D reconstruction
algorithm is used, by which multiple scans are aligned together to obtain a 3D
mesh of the plant, followed by surface area and volume computations. The whole
system, including the programmable growth chamber, robot, scanner, data
transfer and analysis is fully automated in such a way that a naive user can,
in theory, start the system with a mouse click and get back the growth analysis
results at the end of the lifetime of the plant with no intermediate
intervention. As evidence of its functionality, we show and analyze
quantitative results of the rhythmic growth patterns of the dicot Arabidopsis
thaliana(L.), and the monocot barley (Hordeum vulgare L.) plants under their
diurnal light/dark cycles.
