  Modern robotics has enabled the advancement in yield estimation for precision
agriculture. However, when applied to the olive industry, the high variation of
olive colors and their similarity to the background leaf canopy presents a
challenge. Labeling several thousands of very dense olive grove images for
segmentation is a labor-intensive task. This paper presents a novel approach to
detecting olives without the need to manually label data. In this work, we
present the world's first olive detection dataset comprised of synthetic and
real olive tree images. This is accomplished by generating an auto-labeled
photorealistic 3D model of an olive tree. Its geometry is then simplified for
lightweight rendering purposes. In addition, experiments are conducted with a
mix of synthetically generated and real images, yielding an improvement of up
to 66% compared to when only using a small sample of real data. When access to
real, human-labeled data is limited, a combination of mostly synthetic data and
a small amount of real data can enhance olive detection.
