Exploiting diurnal temperature variations to monitor the growth of tubers

Abstract We propose to use diurnal temperature variations for nondestructive monitoring of growing tubers and investigate the feasibility by numerically simulating the data collected with a grid of passive thermal sensors placed in the ground and sampled at regular time 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.