Juan Andrés Paredes

Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning

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.

Development and validation of methods to assess red crown rot (Calonectria ilicicola) severity in soybean: standard area diagram set for roots and diagrammatic scale for canopy

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.

Yield losses associated with peanut smut incidence in Argentina: a quantitative synthesis across field studies

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

A standard area diagram for potato common scab: comparable performance of image- and object-based validation

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.

LLS-SevEst - Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut.

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