Exploiting diurnal temperature variations to monitor the growth of tubers

TL;DR

This paper explores using diurnal temperature variations and passive thermal sensors to noninvasively monitor tuber growth, introducing a new imaging algorithm and an effective inversion method that outperforms traditional approaches.

Contribution

It presents a novel approach combining temperature data and a correlation-based inversion algorithm for tuber monitoring, improving over standard least-squares methods.

Findings

Correlation-based cost functional outperforms least-squares metric

Effective inversion method accurately recovers tuber volume fraction

Passive thermal sensors can non-destructively monitor tuber growth

Abstract

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.