R. Zenkl

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.

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions

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.

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

This paper develops a deep learning method to separate chlorosis and necrosis in wheat canopies, revealing their distinct contributions to greenness decay and enabling genetic studies of disease resistance.