Vegetation Mapping by UAV Visible Imagery and Machine Learning
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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 method can be easily integrated to present VRHA, as much as tools to obtain detailed maps of vegetation.
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