Aerial Image-based Inter-day Registration for Precision Agriculture

Abstract

Satellite imagery has traditionally been used to collect crop statistics, but its low resolution and registration accuracy limit agricultural analytics to plant stand levels and large areas. Precision agriculture seeks analytic tools at near single plant level, and this work explores how to improve aerial photogrammetry to enable inter-day precision agriculture analytics for intervals of up to a month. Our work starts by presenting an accurately registered image time series, captured up to twice a week, by an unmanned aerial vehicle over a wheat crop field. The dataset is registered using photogrammetry aided by fiducial ground control points (GCPs). Unfortunately, GCPs severely disrupt crop management activities. To address this, we propose a novel inter-day registration approach that only relies once on GCPs, at the beginning of the season. The method utilises LoFTR, a state-of-the-art image matching transformer. The original LoFTR network was trained using imagery of outdoor man-made scenes. One of the contributions is to extend LoFTR training method from matching images of a static scene to a dynamic scene of plants undergoing growth. Another contribution is the overall evaluation of our registration method that combines intra-day reconstruction and results from previous days in a seven degree-of-freedom alignment. The results show the benefits of our approach against other matching algorithms and the importance of retraining using crop scenes, particularly using our custom training method for growing crops that achieve an average of 27 cm error across the season.