  An experimental field cropped with sugar-beet with a wide spreading of weeds
has been used to test vegetation identification from drone visible imagery.
Expert masked and hue-filtered pictures have been used to train several Machine
Learning algorithms to develop a semi-automatic methodology for identification
and mapping species at high resolution. Results show that 5m altitude allows
for obtaining maps with an identification efficiency of more than 90%. Such a
method can be easily integrated to present VRHA, as much as tools to obtain
detailed maps of vegetation.
