  The number of panicles (or heads) of Sorghum plants is an important
phenotypic trait for plant development and grain yield estimation. The use of
Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and
analyzing Sorghum images on a large scale. Deep learning can provide methods
for estimating phenotypic traits from UAV images but requires a large amount of
labeled data. The lack of training data due to the labor-intensive ground
truthing of UAV images causes a major bottleneck in developing methods for
Sorghum panicle detection and counting. In this paper, we present an approach
that uses synthetic training images from generative adversarial networks (GANs)
for data augmentation to enhance the performance of Sorghum panicle detection
and counting. Our method can generate synthetic high-resolution UAV RGB images
with panicle labels by using image-to-image translation GANs with a limited
ground truth dataset of real UAV RGB images. The results show the improvements
in panicle detection and counting using our data augmentation approach.
