Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.

中文翻译：

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利用图像分类神经网络检测草坪中的紫莎草并覆盖度估计

准确检测杂草并估计其覆盖范围对于实施精准除草剂施用至关重要。深度学习 (DL) 技术通常用于通过分析像素或单个植物级别的信息来进行杂草检测和覆盖率估计，这需要大量带注释的数据进行训练。本研究旨在评估使用图像分类神经网络（NN）检测和估计狗牙根草坪杂草覆盖率的有效性。