Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022-08
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Mcode
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Language
en
Pages
7
1-7
1-7
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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, Volume 112
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.Description
Funding Information: This study was supported by the SPECTORS project (143081), which is funded by the European cooperation program INTERREG Deutschland-Nederland. It was also partly funded by the AIPSE programme of Academy of Finland through the AI-CropPro project; decision number 315896 (Aalto University, Finland) and 316172 (Luke, Finland). We also would like to acknowledge the computational resources provided by the Aalto Science-IT project and Corinna Roers from Naturschutzzentrum in Kreis Kleve e.V. for labelling the data. Publisher Copyright: © 2022 The Author(s)
Keywords
Deep learning, Rumex, Transfer learning, UAV, Weed detection
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Citation
Valente, J, Hiremath, S, Ariza-Sentís, M, Doldersum, M & Kooistra, L 2022, ' Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning ', International Journal of Applied Earth Observation and Geoinformation, vol. 112, 102864, pp. 1-7 . https://doi.org/10.1016/j.jag.2022.102864