首个真实果园中苹果锈病动态演化的长期数据集，助力精准监测病害发展。
AppleScab-LT: A Longitudinal Real-Field Apple Scab Dataset for Temporal Disease Progression Analysis
- 通过重复跟踪感染叶片，构建长期观测数据序列。
- 含21条病叶轨迹、2101张高分辨率图像，覆盖病害渐进演变过程。
- 适合研究病害演化、农业智能监测及模型开发的科研人员使用。
可靠的植物病害监测系统受限于缺乏在自然田间条件下捕捉病害演化的长期数据集。尽管现有植物病害数据集推动了基于图像的识别技术，但多数仅包含单一时点的静态图像，难以分析病害的时间演化与严重程度进展。为此，本研究提出AppleScab-LT——一个用于追踪苹果锈病长期演化的实地数据集，通过反复观测已标记的感染叶片实现。该数据集依据研究问题驱动框架系统构建、验证与表征，涵盖果园自然环境下的持续监测、叶片个体追踪、专家引导的病害确认、多边形标注、叶片分离、病害严重度量化及时间序列构建。整个数据处理流程采用综合质量保障机制，包括标准化标注协议、专家验证、自动化完整性检查、序列级验证和时间一致性分析。数据集包含21条纵向叶片序列、2101张高分辨率图像及264个连续时间采样点，记录了病害严重度累积、进展速率、监测时长及叶片间差异。基于像素严重度、颜色强度严重度与归一化相对严重度的定量描述符，为时间病害分析提供标准化测量方法。AppleScab-LT为时间病害智能分析、病害进展建模、精准农业与未来作物健康监测提供了可靠资源。
原文摘要 · Abstract (English)
The development of reliable plant disease monitoring systems is constrained by limited longitudinal datasets capturing disease progression under natural field conditions. Although existing plant disease datasets have advanced image-based recognition, most consist of static images acquired at a single time point, limiting analysis of temporal disease evolution and severity progression. To address this gap, this study presents AppleScab-LT, a longitudinal real-field dataset developed to monitor apple scab progression through repeated observations of individually tracked infected leaves. Guided by a research-question-driven framework, the dataset was systematically developed, validated, and characterized for reliable longitudinal disease analysis. AppleScab-LT was constructed through systematic orchard monitoring under natural environmental conditions, incorporating longitudinal leaf tracking, expert-guided disease verification, polygon-based annotation, leaf isolation, disease severity quantification, and temporal sequence construction. A comprehensive quality assurance framework, including standardized annotation protocols, expert validation, automated integrity checks, sequence-level verification, and temporal consistency analysis, was applied throughout curation. The dataset contains 21 longitudinal leaf sequences, 2,101 high-resolution images, and 264 progressive temporal samples from repeated monitoring of same infected leaves. It captures variability in severity accumulation, progression rates, monitoring duration, and inter-leaf progression. Quantitative disease descriptors based on pixel severity, color-intensity severity, and normalized relative severity provide standardized measurements for temporal disease analysis. AppleScab-LT provides a reliable resource for temporal disease intelligence, disease progression modelling, precision agriculture, and future crop health monitoring
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