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.

翻译：精密设备在提升农业系统生产质量与生产效率方面发挥着重要作用。因此，在精准农业中优化这些设备至关重要。近年来，随着深度学习技术的进步，已有若干研究致力于利用其能力改进喷雾系统性能。然而，这些方法的有效性严重依赖于训练数据集的规模，而数据收集成本高昂且耗时。为解决训练样本不足的挑战，我们开发了一种名为DropletGAN的图像生成器用于生成液滴图像。DropletGAN模型使用高速相机采集的小型数据集进行训练，能够生成分辨率逐步提升的图像。实验结果表明，该模型可生成尺寸为1024×1024的高质量图像。通过Fr\'echet起始距离（FID）对DropletGAN生成的图像进行评估，其FID得分为11.29。此外，本研究利用计算机视觉与深度学习领域的最新进展，基于合成数据集开发了轻量级液滴检测器。实验证明，使用合成数据集后检测模型的平均精度均值（mAP）提升了16.06%。据我们所知，本研究首次采用生成模型增强液滴检测能力。其意义不仅在于优化喷嘴设计以构建高效喷雾系统，更在于解决了精准农业各类任务中普遍存在的数据不足难题。这项工作为在追求农业实践最优化与可持续性的同时节约资源，提供了重要贡献。