面向葡萄叶病害分类与检测的深度学习方法的以数据集为中心的基准测试

中文摘要 AI 辅助

葡萄叶病害识别对精准农业至关重要，可实现早期诊断、及时干预和葡萄园管理的优化。尽管深度学习已取得优异成果，但许多研究依赖少量数据集，这些数据集通常在受控条件下采集，可能无法反映葡萄园的实际挑战，如复杂背景、可变光照、遮挡、叶片姿态、病害严重程度和设备差异。本文提出一种面向葡萄叶病害分类与检测的深度学习方法的以数据集为中心的基准测试。我们从病害类别、标注类型、采集条件、图像特征、类别分布、来源和任务适用性等方面分析公开可用的数据集。在三种场景下评估代表性模型：图像级分类、区域级分类和目标检测。分类采用准确率评估，检测采用mAP@50和mAP@50:95评估。跨数据集实验进一步研究具有兼容病害类别但视觉和标注特征不同的数据集间的迁移。结果显示，在多个受控或衍生数据集上分类性能接近饱和，在异构数据集上难度更大，不同标注设置下检测性能差异显著。跨数据集性能急剧下降，尤其是目标检测，表明共享病害标签不一定定义等效的识别任务。该基准测试强调数据集来源、真实田间评估、标注兼容性和外部验证，以实现可靠的葡萄园病害识别。

英文摘要

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.

发表机构

• Faculty of Computer Science and Engineering, University Ss Cyril and Methodius（圣西里尔与美多德大学计算机科学与工程学院）

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