AppleScab-LT：用于时间病害进展分析的纵向真实苹果黑星病数据集

中文摘要 AI 辅助

可靠的植物病害监测系统的发展受到自然田间条件下捕捉病害进展的纵向数据集有限的限制。尽管现有的植物病害数据集在基于图像的识别方面取得了进展，但大多数由在单一时间点获取的静态图像组成，限制了对时间病害演变和严重程度进展的分析。为了填补这一空白，本研究提出了AppleScab-LT，这是一个纵向真实田间数据集，用于通过对单独跟踪的受感染叶片的重复观察来监测苹果黑星病的进展。在研究问题驱动的框架指导下，该数据集被系统地开发、验证和表征，以用于可靠的纵向病害分析。AppleScab-LT是通过在自然环境条件下进行系统的果园监测构建的，整合了纵向叶片跟踪、专家指导的病害验证、基于多边形的注释、叶片隔离、病害严重程度量化和时间序列构建。在整个整理过程中应用了全面的质量保证框架，包括标准化注释协议、专家验证、自动完整性检查、序列级验证和时间一致性分析。该数据集包含21个纵向叶片序列、2101张高分辨率图像，以及来自相同受感染叶片重复监测的264个渐进时间样本。它捕捉了严重程度积累、进展速率、监测持续时间和叶片间进展的变异性。基于像素严重程度、颜色强度严重程度和归一化相对严重程度的定量病害描述符为时间病害分析提供了标准化测量。AppleScab-LT为时间病害智能、病害进展建模、精准农业和未来作物健康监测提供了可靠资源。

英文摘要

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

发表机构

• National Institute of Technology, Hazratbal Srinagar（斯里那加国家理工学院）
• Indian Institute of Technology, Ropar（罗帕尔印度理工学院）

机构由 AI 辅助整理，请以论文原文为准。