无需标注数据，通过识别作物行实现精准杂草定位
RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection
- 利用作物行信息生成伪标签，训练轻量模型区分杂草与作物
- 在WeedMap数据集上达到75.3的F1分数，优于多个基线方法
- 适合农业无人机实时巡检，助力大田精准除草
精准农业依赖有效的杂草管理以保障作物产量。本文提出RoWeeder，一种结合作物行检测与抗噪深度学习模型的无监督杂草映射框架。通过作物行信息构建伪真值，训练轻量级深度学习模型，在噪声数据下仍能有效区分作物与杂草。在WeedMap数据集上的评估显示，RoWeeder的F1得分为75.3，优于多个基线方法。全面的消融实验验证了模型性能。将RoWeeder与无人机技术结合，可实现大田的实时空中巡查，支持精准杂草管理。代码已开源： https://github.com/pasqualedem/RoWeeder。
原文摘要 · Abstract (English)
Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning model. By leveraging crop-row information to create a pseudo-ground truth, our method trains a lightweight deep learning model capable of distinguishing between crops and weeds, even in the presence of noisy data. Evaluated on the WeedMap dataset, RoWeeder achieves an F1 score of 75.3, outperforming several baselines. Comprehensive ablation studies further validated the model's performance. By integrating RoWeeder with drone technology, farmers can conduct real-time aerial surveys, enabling precise weed management across large fields. The code is available at: \url{https://github.com/pasqualedem/RoWeeder}.
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