Achim Walter

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.

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 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.

This study compares multispectral and RGB sensors on UAVs for monitoring wheat canopy senescence, finding that frequent measurements are more effective than high spectral resolution.

This study uses thermal imaging to assess stay-green functionality in wheat canopies under temperate conditions, finding measurable temperature differences linked to leaf activity traits.

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