Automated georeferencing of grapevine images using visual tracking and satellite positioning for precision viticulture

Abstract Precise georeferencing of grapevines is essential for plant-level monitoring in precision viticulture. We address the task of assigning side-view images, captured from a vehicle with GNSS logging, to individual vines previously mapped with high-precision GNSS. The challenge is to select, for each vine, the frame where it is most centrally and fully visible. We present a method for automated image assignment, combining a vision-based detection and tracking method with optional GNSS-based correction and validation. Selected rows were manually annotated as ground truth references for evaluation. Results show that our method reliably assigns the correct frames in most cases, highlighting its potential for scalable precision agriculture. An advantage of this approach is that it does not require manual offset correction to account for varying growing directions.