Journal
EUROPEAN JOURNAL OF REMOTE SENSING
Volume 54, Issue -, Pages 71-88Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/22797254.2020.1793687
Keywords
Weed mapping; grassland management; open-source; unmanned aerial vehicle (UAV); RGB imagery; neural network
Categories
Funding
- cooperation program INTERREG Deutschland-Nederland [143081]
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A proposed open-source workflow utilized a commercially available UAV for automated weed mapping in a nature reserve, achieving high accuracy and F1 scores in early weed detection. This method showed potential for semi- or fully automated early weed detection system in grasslands using UAV-imagery.
Weed control is one of the biggest challenges in organic farms or nature reserve areas where mass spraying is prohibited. Recent advancements in remote sensing and airborne technologies provide a fast and efficient means to support environmental monitoring and management, allowing early detection of invasive species. However, in order to perform weed classification, current studies mostly relied on object-based image analysis (OBIA) and proprietary software which required substantial human inputs. This paper proposes an open-source workflow for automated weed mapping using a commercially available unmanned aerial vehicle (UAV). The UAV was flown at a low altitude between 10 m and 20 m, and collected true-colour RGB imagery over a weed-infested nature reserve. The aim of this study is to develop a repeatable and robust system for early weed detection, with minimum human intervention, for classification ofRumex obtusifolius(R. obtusifolius). Preliminary results of the proposed workflow achieved an overall accuracy of 92.1% with an F1 score of 78.7%. The approach also demonstrated the capability to mapR. obtusifoliusin datasets collected at various flight altitudes, camera settings and light conditions. This shows the potential to perform semi- or fully automated early weed detection system in grasslands using UAV-imagery.
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