Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning

Abstract

Rumex obtusifolius (Rumex or broad leaved dock) is one of the most common weeds in grasslands. It spreads quickly, lowers the nutritional value of the grass, and is poisonous for livestock due to its oxalic acid content. Mapping it is important before any control treatment is applied. Current methods for mapping Rumex either involve manual work or the utilization of ground robots, which are not efficient in large fields. This study investigated the feasibility of using aerial images from unmanned aerial vehicles (UAV) and deep learning to map Rumex in grasslands. Seven pre-trained CNN models were tested using transfer learning on UAV images acquired at 10 m, 15 m, and 30 m height. Based on Cross Validation results, MobileNet performed the best in detecting Rumex, with an F1-Score of 78.36% and an AUROC of 93.74%, at 10 m height. At 15 m, the detection performance was relatively lower (F1-score = 72.00%, AUROC = 88.67%), but the results showed that the performance can increase with more data. Experiments also showed that Rumex detection was dependent on the flight height since the algorithm was unable to detect the plants at 30 m height. The code and the datasets used in this work were released in an open access repository to contribute to the advances in grassland management using UAV technology.