An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: using Rumex obtusifolius as a case study

• Published in: European journal of remote sensing Vol. 54; no. sup1; pp. 71 - 88
• Main Authors: Lam, Olee Hoi Ying, Dogotari, Marcel, Prüm, Moritz, Vithlani, Hemang Narendra, Roers, Corinna, Melville, Bethany, Zimmer, Frank, Becker, Rolf
• Format: Journal Article
• Language: English
• Published: Cagiari Taylor & Francis 05.02.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Airborne sensing; Automation; Color imagery; Environmental management; Environmental monitoring; grassland management; Grasslands; Image analysis; Image classification; Invasive species; Low altitude; Mapping; Nature reserves; neural network; Open source software; open-source; Organic farming; Remote sensing; RGB imagery; Spraying; Sustainable agriculture; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Weed control; Weed mapping; Weeds; Workflow
• ISSN: 2279-7254; 2279-7254
• DOI: 10.1080/22797254.2020.1793687

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 of Rumex 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 map R. obtusifolius in 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.