Robust QR Code Detection in Oblique Aerial Images for Agricultural Field Experiments

Field experiments in precision agriculture require reliable linkage of plant variants to experimental plots, which is difficult in oblique aerial imagery because the drone position does not identify the imaged ground location. We present and evaluate a reproducible workflow for detecting QR reference markers in high-resolution UAV images acquired over a wheat field before crop growth at a 45° viewing angle. The workflow combines manually defined regions of interest, documented preprocessing, a rotation sweep, and independent evaluation of OpenCV and pyzbar decoding backends. QR codes were selected for practical field deployment because their payload can directly encode a plot identifier and can be checked with an ordinary smartphone; the study does not benchmark QR codes against other fiducial-marker families. In 500 field-collected QR-code crops from 20 images, 491 codes (98.2%) were decoded; all nine failures had incomplete code visibility. The observed result is therefore a system-level outcome of both image acquisition and decoding under the tested configuration, rather than a growing-season operational accuracy. The mean processing time was approximately 0.41 s per region of interest on a standard PC. The work demonstrates QR-marker detection under the stated pre-growth conditions and provides a basis for future plot-label reference, calibration, and downstream Fusarium Head Blight phenotyping.