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 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.

摘要

在禁止大量喷洒的有机农场或自然保护区，杂草控制是最大的挑战之一。遥感和机载技术的最新进展提供了一种快速有效的手段来支持环境监测和管理，从而可以及早发现入侵物种。但是，为了进行杂草分类，当前的研究主要依靠基于对象的图像分析（OBIA）和专有软件，这些软件需要大量的人工输入。本文提出了一种开放源代码的工作流程，用于使用市售的无人机（UAV）自动进行杂草测绘。无人机在10 m至20 m的低空飞行，并在杂草为生的自然保护区上收集了真彩色RGB图像。Rumex obtusifolius（R. obtusifolius）。拟议工作流程的初步结果实现了92.1％的总体准确率，F1分数为78.7％。该方法还展示了在各种飞行高度，相机设置和光照条件下收集的数据集中绘制钝角红景天的能力。这显示了使用无人机图像在草原上进行半或全自动早期杂草检测系统的潜力。