基于YOLO的精准农业杂草检测多数据集基准

中文摘要 AI 辅助

杂草检测是精准农业的重要组成部分，能够实现针对特定地点的杂草管理并减少不必要的除草剂使用。尽管深度学习方法在作物和杂草检测方面取得了显著成果，但许多研究依赖于单一数据集评估，难以评估在不同农业领域的鲁棒性。本文提出了一个用于精准农业杂草检测的深度目标检测器多数据集基准，重点评估了YOLO26模型。我们在七个公开的杂草检测数据集上评估了nano、small和medium变体，这些数据集涵盖了不同的作物、杂草种类、田间条件、采集设置和标注协议。模型在检测精度、模型复杂度、推理延迟、FPS和模型大小方面进行了比较。除了数据集内评估，我们还使用统一的一类杂草设置研究了跨域泛化，并使用所有数据集的组合训练子集评估了多源训练。结果表明，YOLO26在数据集内表现强劲，其中YOLO26m获得了最高的平均精度，而YOLO26s提供了最佳的实际精度-效率权衡。然而，跨域性能显著下降，YOLO26s的平均域内mAP$_{50:95}$从0.603降至域外设置中的0.148。多源训练在多个数据集上提高了性能，但并未完全消除域偏移。总体而言，该基准强调了数据集多样性、域相似性和目标域适应对于在真实世界精准农业应用中实现稳健杂草检测的重要性。

英文摘要

Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Although deep learning methods have achieved strong results for crop and weed detection, many studies rely on single-dataset evaluation, making it difficult to assess robustness across different agricultural domains. This paper presents a multi-dataset benchmark of deep object detectors for weed detection in precision agriculture, with a focused evaluation of YOLO26 models. We evaluate nano, small, and medium variants on seven public weed-detection datasets covering different crops, weed species, field conditions, acquisition setups, and annotation protocols. The models are compared in terms of detection accuracy, model complexity, inference latency, FPS, and model size. In addition to in-dataset evaluation, we investigate cross-domain generalization using a unified one-class weed setup and evaluate multi-source training using the combined training subsets from all datasets. The results show that YOLO26 achieves strong in-dataset performance, with YOLO26m obtaining the highest average accuracy and YOLO26s providing the best practical accuracy-efficiency trade-off. However, cross-domain performance decreases substantially, with YOLO26s dropping from an average in-domain mAP$_{50:95}$ of 0.603 to 0.148 in the off-domain setting. Multi-source training improves performance on several datasets, but does not fully eliminate domain shift. Overall, the benchmark highlights the importance of dataset diversity, domain similarity, and target-domain adaptation for robust weed detection in real-world precision agriculture applications.

发表机构

• Faculty of Computer Science and Engineering, University Ss Cyril and Methodius（计算机科学与工程学院，圣西里尔和梅多迪乌斯大学）

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