Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training data. The reality of scarcity of well-annotated training data, however, is often addressed through generating more data using data augmentation. Nevertheless, conventional augmentation techniques such as random flipping, color changes, and blurring lack sufficient fidelity and diversity. This paper investigates a generative AI-based augmentation technique that uses the Stable Diffusion model to produce diverse synthetic images that improve the quantity and quality of training datasets for weed detection models. Moreover, this paper explores the impact of these synthetic images on the performance of real-time detection systems, thus focusing on compact CNN-based models such as YOLO nano for edge devices. The experimental results show substantial improvements in mean Average Precision (mAP50 and mAP50-95) scores for YOLO models trained with generative AI-augmented datasets, demonstrating the promising potential of synthetic data to enhance model robustness and accuracy.

翻译：精准杂草管理对于维持作物生产力和生态平衡至关重要。传统除草剂施用面临经济与环境挑战，这凸显了基于深度学习的智能杂草控制系统的重要性。此类系统需要大量高质量训练数据。然而，实际中标注良好的训练数据稀缺的问题，通常通过数据增强技术生成更多数据来解决。尽管如此，传统增强技术（如随机翻转、颜色变换和模糊处理）缺乏足够的保真度和多样性。本文研究了一种基于生成式人工智能的数据增强技术，该技术利用Stable Diffusion模型生成多样化的合成图像，以提高杂草检测模型训练数据集的数量与质量。此外，本文探究了这些合成图像对实时检测系统性能的影响，重点关注适用于边缘设备的紧凑型CNN模型（如YOLO nano）。实验结果表明，使用生成式人工智能增强数据集训练的YOLO模型在平均精度均值（mAP50与mAP50-95）指标上取得显著提升，证明了合成数据在增强模型鲁棒性与准确性方面的巨大潜力。