Radek Zenkl

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.

A 1,096-image pixel-labelled wheat organ dataset plus 52,078 unlabelled field images from 11 institutions enables Segformer leaf and spike segmentation near 90% mIoU, while stems lag.

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.

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.