  The possibility to use diurnal temperature variations for nondestructive
monitoring of growing tubers is investigated by numerically simulating the data
collected with a grid of passive thermal sensors placed in the ground and
sampled at regular intervals. A qualitative linear imaging algorithm that
produces an approximate projected view of the tubers is proposed and an
effective inversion method is applied to recover the volume fraction of tubers.
In particular, it is shown that a correlation-based cost functional outperforms
the usual least-squares metric, although, requiring additional steps to deal
with the non-uniqueness of the solution.
