Juan Andrés Paredes

This paper uses high-resolution satellite imagery and machine learning to map Red Crown Rot in soybean fields, achieving high accuracy in detecting affected canopies.

This paper develops and validates methods to assess red crown rot severity in soybean using root imaging and visual scales, improving accuracy and agreement across environments.

The paper quantifies yield losses from peanut smut in Argentina using field studies and finds consistent damage coefficients across incidence levels.

This study develops and validates a standard area diagram (SAD) for quantifying potato common scab severity, showing comparable accuracy between real tubers and digital images.

This paper develops an automated image analysis model, LLS-SevEst, to quantify late leaf spot severity in peanut leaves using image segmentation techniques.

This paper conducts a scientometric analysis of peanut smut research, identifying trends in publication, topics, collaboration, and impact, while highlighting gaps in peer-reviewed scientific output.