L. G. da Silva

Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management

This study models soil organic carbon in the Andes using machine learning with crop rotation data, finding that fallow frequency is a key predictor of SOC when soil data are excluded.

Prediction of late blight severity in a large panel of potato genotypes using low-altitude aerial images and machine learning methods

This study uses UAV-derived multispectral imagery and machine learning to predict late blight severity in potato genotypes, showing improved accuracy over traditional methods.