Jonas Anderegg

A Novel Phenotyping Approach for Reconciling Precision and Variance in Disease Severity Estimates from High-resolution Imaging

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.

Tracking Lesion Growth in the Field: Imaging and Deep Learning Reveal Components of Quantitative Resistance

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.

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.