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, this paper proposes an alternative solution by
generating artificial images of droplets using generative adversarial networks
(GAN). The GAN 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 $1024\times1024$. 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.

该研究介绍了如何利用生成敌对网络来生成农业喷雾系统中的液滴图像，并利用这些合成数据集发展了一个轻量级液滴检测模型，从而提高了平均准确度（mAP）达到了 16.06%。这是第一个应用生成模型增强液滴检测的研究，对于优化喷嘴设计以构建高效喷雾系统，并解决各种精确农业任务中数据不足的共同挑战具有重要意义，为达到最佳和可持续的农业实践做出了重要贡献。