  The accurate identification of walnuts within orchards brings forth a
plethora of advantages, profoundly amplifying the efficiency and productivity
of walnut orchard management. Nevertheless, the unique characteristics of
walnut trees, characterized by their closely resembling shapes, colors, and
textures between the walnuts and leaves, present a formidable challenge in
precisely distinguishing between them during the annotation process. In this
study, we present a novel approach to improve walnut detection efficiency,
utilizing YOLOv5 trained on an enriched image set that incorporates both real
and synthetic RGB and NIR images. Our analysis comparing results from our
original and augmented datasets shows clear improvements in detection when
using the synthetic images.
