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

Abstract

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 truecolour 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.

Hybrid CNN Model for Classification of Rumex Obtusifolius in Grassland

“…This limits the method's performance to identifying Rumex in various realworld conditions. Lam et al [22] used the Visual Graphics Group (VGG) method to classify the early growth of Rumex weeds using UAV. One of the limitations of their method is focusing on limited cases of Rumex that are found on one field site and ignoring other cases such as the different growth stages and other real conditions.…”

A Survey of Deep Learning Techniques for Weed Detection from Images

“…H. Huang et al (2018a) collected images with a resolution of 3000×4000 pixels using a sequence of forward-overlaps and side-overlaps to cover the entire field. Lam et al (2020) flew DJI Phantom 3 and 4 Pro drones with a RGB camera at three different heights (10, 15 and 20 m) to determine the optimal height for weed detection.…”

“…Similar techniques were used byHall et al (2018) Yu et al (2019a). separated the collected images into two parts; one with positive images that contained weeds, and the other of negative images without weeds Lam et al (2020). proposed an object-based approach to generate labelled data.…”

“…Abdalla et al (2019),Adhikari et al (2019),Andrea et al (2017),Asad and Bais (2019),Bini et al (2020),Bosilj et al (2020),Brilhador et al (2019),Chechlinski et al (2019), Farooq et al (2019), Fawakherji et al (2019), Hall et al (2018, H Huang et al (2018a), H. Huang et al (2018b,. H Huang et al (2020),Ishak et al (2007),Knoll et al (2019),Kounalakis et al (2019),Lam et al (2020),Liakos et al (2018),Lottes et al (2020),Lottes et al (2018b), Milioto et al (2017,Osorio et al (2020),Patidar et al (2020),Ramirez et al (2020),Rist et al (2019), andSa et al (2018),Skovsen et al (2019),Umamaheswari and Jain (2020)…”

Demystifying the Differences between Structure-from-MotionSoftware Packages for Pre-Processing Drone Data

“…Drone data have been captured to provide information across a range of environmental fields, predominantly to assess vegetation coverage, composition, and/or structure in the terrestrial environment (e.g., [4][5][6][7][8]). However, they have also been used to study a range of other environments, including mangroves [9][10][11], oyster reefs [12], coral reefs [13,14], coastal dunes [15,16], and seagrass beds [17].…”