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 [1], a state-of-the-art image-matching transformer. The original LoFTR network was trained using imagery of outdoor urban areas. One of our contributions is to extend LoFTR’s training method, which uses matching images of a static scene, to a dynamic scene of plants undergoing growth. Another contribution is a thorough evaluation of our registration method that integrates intraday crop reconstruction with earlier-day scans in a seven degree-of-freedom alignment. Experimental results show the advantage of our approach over other matching algorithms and demonstrate the importance of retraining using crop scenes, and a training method customised for growing crops, with an average registration error of 27 cm across a season.