Jonas Anderegg

The paper develops an imaging approach to improve disease severity estimation in wheat by combining high-resolution imaging and focus bracketing, reducing measurement uncertainty and enabling efficient sampling.

The paper uses deep learning to track lesion growth in wheat infected by Zymoseptoria tritici, finding lesion growth is a significant component of quantitative resistance in most cultivars.

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.