2022/05/23 by Giuliano Vitali, Vitali, Giuliano
Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.2 #I.4 #J.3 #Machine Learning (stat.ML) #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Species Distribution and Climate Change
paper · pdf · doi:10.48550/arxiv.2205.11061
openalex publication_date 2022/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.