Achim Walter

From Canopy Images to Organ-Level Disease Assessments: A Scalable Approach to Measure Quantitative Resistance in the Field

This paper introduces an imaging protocol and pipeline to measure wheat disease severity from canopy images, achieving high correlation with manual methods and enabling scalable, contact-free disease assessment.

Towards high throughput in-field detection and quantification of wheat foliar diseases with deep learning

This paper develops a deep learning method to detect and quantify wheat foliar diseases using field images, achieving high accuracy in keypoint detection and segmentation.

The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years

The paper introduces a large-scale wheat phenotyping dataset with time series imaging and trait data across six years, enabling studies on genotype-environment interactions and crop modeling.

Genetic determination of stomatal patterning in winter wheat (Triticum aestivum L.)

This study identifies genetic loci associated with stomatal density in winter wheat, providing targets for improving water use efficiency through marker-assisted selection.