  Precision devices play an important role in enhancing production quality and
productivity in agricultural systems. Therefore, the optimization of these
devices is essential in precision agriculture. Recently, with the advancements
of deep learning, there have been several studies aiming to harness its
capabilities for improving spray system performance. However, the effectiveness
of these methods heavily depends on the size of the training dataset, which is
expensive and time-consuming to collect. To address the challenge of
insufficient training samples, we developed an image generator named DropletGAN
to generate images of droplets. The DropletGAN model is trained by using a
small dataset captured by a high-speed camera and capable of generating images
with progressively increasing resolution. The results demonstrate that the
model can generate high-quality images with the size of 1024x1024. The
generated images from the DropletGAN are evaluated using the Fr\'echet
inception distance (FID) with an FID score of 11.29. Furthermore, this research
leverages recent advancements in computer vision and deep learning to develop a
light droplet detector using the synthetic dataset. As a result, the detection
model achieves a 16.06% increase in mean average precision (mAP) when utilizing
the synthetic dataset. To the best of our knowledge, this work stands as the
first to employ a generative model for augmenting droplet detection. Its
significance lies not only in optimizing nozzle design for constructing
efficient spray systems but also in addressing the common challenge of
insufficient data in various precision agriculture tasks. This work offers a
critical contribution to conserving resources while striving for optimal and
sustainable agricultural practices.
