M

Machine Learning

Using non-invasive sensors to predict hypoglycaemic conditions as an alternative to invasive CGM

Analysis of the impact of sowing date, temperature, soil type, and digital sowing recommendation on cotton crop growth and yield

Workflow repository for the HYDRA-EO project, hosting processing pipelines, scripts, and documentation supporting hybrid machine learning and radiative transfer modelling for crop stress, pests, and disease monitoring using multi-sensor Earth Observation data.

ACT project for the masters programme Geo-Information Science at Wageningen University & Research, conducted for the Dutch Ministry of Defence