Abstract
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.
Abstract (translated)
精密设备在农业系统中提高生产质量和生产力的作用非常重要。因此，优化这些设备对于精准农业来说至关重要。近年来，随着深度学习的进步，已经有一些研究试图利用其能力来提高喷雾系统性能。然而，这些方法的有效性很大程度上取决于训练数据集的大小，这需要花费大量的时间和金钱来收集。为解决训练样本不足的问题，本文提出了一种通过生成对抗网络（GAN）生成雾滴的人工图像来替代现有方案的解决方案。GAN模型通过使用高速相机捕获的小数据集进行训练，能够生成具有逐渐增加分辨率的图像。结果显示，该模型可以生成$1024\times1024$大小的优质图像。此外，本文利用计算机视觉和深度学习的最新进展，开发了一种使用合成数据集的轻型雾滴检测器。结果表明，当利用合成数据集时，检测模型平均精准度（mAP）增长了16.06%。据我们所知，这项工作是第一个采用生成模型来增强雾滴检测的。其重要性不仅在于优化雾滴喷嘴设计以构建高效的喷雾系统，而且在于解决各种精准农业任务中数据不足的常见挑战。这项工作为在寻求 optimal and sustainable agricultural practices的同时保护资源做出了重要的贡献。
URL
https://arxiv.org/abs/2402.15909
