  In the industry, the value of wood-logs strongly depends on their internal
structure and more specifically on the knots' distribution inside the trees. As
of today, CT-scanners are the prevalent tool to acquire accurate images of the
trees internal structure. However, CT-scanners are expensive, and slow, making
their use impractical for most industrial applications. Knowing where the knots
are within a tree could improve the efficiency of the overall tree industry by
reducing waste and improving the quality of wood-logs by-products. In this
paper we evaluate different deep-learning based architectures to predict the
internal knots distribution of a tree from its outer-shape, something that has
never been done before. Three types of techniques based on Convolutional Neural
Networks (CNN) will be studied.
  The architectures are tested on both real and synthetic CT-scanned trees.
With these experiments, we demonstrate that CNNs can be used to predict
internal knots distribution based on the external surface of the trees. The
goal being to show that these inexpensive and fast methods could be used to
replace the CT-scanners.
  Additionally, we look into the performance of several off-the-shelf
object-detectors to detect knots inside CT-scanned images. This method is used
to autonomously label part of our real CT-scanned trees alleviating the need to
manually segment the whole of the images.
