高分辨率的叶子图像序列与几何对齐,用于动态表型的叶片疾病
Jonas Anderegg1, Bruce A McDonald2
1Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland. jonas.anderegg@agroscope.admin.ch.
Scientific data
|January 23, 2026
概括
一个由12520张小麦叶图像组成的新数据集捕捉了随着时间的推移疾病的发展. 本资源有助于了解植物疾病耐药性和改善农业的诊断工具.
科学领域:
- 植物病理学 植物病理学
- 计算生物学 计算生物学
- 农业科学 农业科学
背景情况:
- 准确的,大规模的植物疾病表型化对于理解耐药性机制和改善作物诊断至关重要.
- 当前的成像和图像处理技术提供了改进,但对于实际的高通量应用需要进一步的进步.
研究的目的:
- 为了呈现一个全面的数据集的高分辨率RGB图像小麦叶与发展疾病症状.
- 提供相关的元数据和工具,以促进植物疾病动态和计算方法的研究.
主要方法:
- 收集了12520张高分辨率RGB图像,涵盖了1032个表现出疾病症状的小麦叶的时间序列.
- 在几何上对齐所有图像,精确度低于毫米.
- 包括转换矩阵,症状细分面具和全面的元数据 (治疗,天气,表态,疾病发生).
主要成果:
- 开发了一个数据集,允许详细调查叶子级疾病动态,包括病变和的出现率和增长.
- 提供了一个轻量级的Python工具包,用于有效加载,对齐,检查和编辑图像序列.
- 建立了开发植物疾病检测和跟踪先进图像分析方法的基础.
结论:
- 提出的数据集和工具包显著提高了植物病的时间解析表型能力.
- 本资源支持开发用于疾病症状分析和跟踪的新型计算框架.
- 促进了对植物病原体相互作用的更深入的了解,并开发了强大的疾病抵抗策略.
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