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High-Resolution Leaf Image Sequences with Geometric Alignment for Dynamic Phenotyping of Foliar Diseases.

Jonas Anderegg1, Bruce A McDonald2

  • 1Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland. jonas.anderegg@agroscope.admin.ch.

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|January 23, 2026
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Summary

A new dataset of 12,520 wheat leaf images captures disease development over time. This resource aids in understanding plant disease resistance and improving diagnostic tools for agriculture.

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Area of Science:

  • Plant Pathology
  • Computational Biology
  • Agricultural Science

Background:

  • Accurate, large-scale phenotyping of plant diseases is crucial for understanding resistance mechanisms and improving crop diagnostics.
  • Current imaging and image processing techniques offer improvements but require further advancements for practical, high-throughput applications.

Purpose of the Study:

  • To present a comprehensive dataset of high-resolution RGB images of wheat leaves with developing disease symptoms.
  • To provide associated metadata and tools to facilitate research into plant disease dynamics and computational methods.

Main Methods:

  • Collected 12,520 high-resolution RGB images across 1,032 time series of wheat leaves exhibiting disease symptoms.
  • Geometrically aligned all images with sub-millimeter precision.
  • Included transformation matrices, symptom segmentation masks, and comprehensive metadata (treatments, weather, phenology, disease occurrence).

Main Results:

  • Developed a dataset enabling detailed investigation of leaf-level disease dynamics, including lesion and pustule emergence rates and growth.
  • Provided a lightweight Python toolkit for efficient loading, alignment, inspection, and editing of image sequences.
  • Established a foundation for developing advanced image analysis methods for plant disease detection and tracking.

Conclusions:

  • The presented dataset and toolkit significantly advance the capacity for time-resolved phenotyping of plant diseases.
  • This resource supports the development of novel computational frameworks for disease symptom analysis and tracking.
  • Facilitates deeper insights into plant-pathogen interactions and the development of robust disease resistance strategies.