摘要：葡萄叶病害识别对于精准农业十分重要，可实现早期诊断、及时干预并改善葡萄园管理。尽管深度学习已经取得了较强的结果，但许多研究依赖数量较少的数据集，且这些数据集通常在受控条件下采集，可能无法反映真实葡萄园中的挑战，例如复杂背景、光照变化、遮挡、叶片姿态、病害严重程度以及设备差异。本文提出了一个以数据集为中心的葡萄叶病害分类与检测深度学习方法基准。我们从病害类别、标注类型、采集条件、图像特征、类别分布、数据来源及任务适用性等方面分析了公开可用的数据集。研究在三种设置下评估了具有代表性的模型：图像级分类、区域级分类和目标检测。分类任务使用准确率评估，检测任务则使用 mAP@50 和 mAP@50:95 评估。跨数据集实验进一步考察了病害类别兼容、但视觉特征和标注特性不同的数据集之间的迁移能力。结果表明，多个受控环境或衍生数据集上的分类性能已接近饱和；异质性数据集的难度更高；不同标注设置下的检测性能存在显著差异。跨数据集性能明显下降，尤其是在目标检测任务中，这表明共享的病害标签并不一定定义了等价的识别任务。该基准强调，为实现可靠的葡萄园病害识别，应重视数据集来源、真实田间评测、标注兼容性和外部验证。

Abstract:Grape leaf disease recognition is important for precision agriculture, enabling early diagnosis, timely intervention, and improved vineyard management. Although deep learning has achieved strong results, many studies rely on few datasets, often acquired under controlled conditions, and may not reflect real vineyard challenges such as complex backgrounds, variable illumination, occlusion, leaf pose, disease severity, and device differences. This paper presents a dataset-centric benchmark of deep learning methods for grape leaf disease classification and detection. We analyze publicly available datasets in terms of disease categories, annotation types, acquisition conditions, image characteristics, class distributions, provenance, and task suitability. Representative models are evaluated in three settings: image-level classification, region-level classification, and object detection. Classification is assessed using accuracy, while detection is evaluated using mAP@50 and mAP@50:95. Cross-dataset experiments further examine transfer between datasets with compatible disease categories but different visual and annotation characteristics. Results show near-saturated classification performance on several controlled or derivative datasets, greater difficulty on heterogeneous datasets, and substantial variation in detection performance across annotation settings. Cross-dataset performance drops sharply, especially for object detection, indicating that shared disease labels do not necessarily define equivalent recognition tasks. The benchmark emphasizes dataset provenance, realistic field evaluation, annotation compatibility, and external validation for reliable vineyard disease recognition.