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.
Rumex obtusifolius (R. obtusifolius) is one of the most common non-cultivated weed in European grasslands. Its broad-leaved and wide-spread nature make this weed competitive with the native pasture species reducing grass yield (van Evert et al. 2010), while its oxalic acid content makes this species poisonous for livestock if large doses are consumed (Hejduk and Doležal 2004). Therefore, early removal is preferred especially in organic dairy farms or conservation areas where mass spraying is prohibited. Remote sensing and airborne technologies offer fast and efficient support in environmental monitoring allowing early detection of invasive species, yet current studies mostly rely on object-based image analysis (OBIA) and proprietary software to perform weed classification that require substantial human inputs. In this work, an open source workflow for automatic weed detection using unmanned aerial vehicle (UAV) RGB-imagery of native grassland had been developed using deep learning techniques, based on a previously developed OBIA approach (Lam et al. 2019). During the study, DJI Phantom 3 and 4 Pro were used for data acquisition throughout the vegetation period in 2018 and early 2019 at a nature conversation area in North Rhine-Westphalia, Germany. Images were processed using OpenDroneMap to produce orthomosaics. OBIA methods were then performed using Python and QGIS to assist the data labelling process for training a convolutional neural network (CNN), which was later used as an image classifier. Preliminary results of the proposed workflow achieved an overall accuracy of 93.8% and had demonstrated the capability in mapping R. obtusifolius in datasets collected at various flight altitudes, camera settings and light conditions. This shows the potential of developing a repeatable and robust system for semi-or fully-automated early weed detection in grassland using UAV-imagery.
<p>Plant traits - morphological, anatomical, biochemical, physiological or phenological features measurable at the level of individuals or their component organs or tissues - reflect the outcome of evolutionary and community assembly processes responding to abiotic and biotic environmental constraints. Therefore, measurements of plant traits and trait syndromes (consistent associations of plant traits) are valuable observations to evaluate models based on eco-evolutionary optimality (EEO) principles. In 2007 the TRY database project (https://www.try-db.org/) was initiated to improve the empirical basis for trait-based ecological studies, trying to bring together the different plant trait databases worldwide. As a result, the TRY Plant Trait Database has constantly been growing and has accomplished unprecedented coverage. Since 2019 the data are publicly available under a CC BY license. This presentation is supposed to provide an update on recent developments in the context of the TRY initiative, i.e. the recently released new version of the TRY database (version 6), the release of the 'Global Spectrum of Plant Form and Function Dataset', and the 'rtry' R package to support preprocessing of trait data retrieved from the TRY database.</p>
<p>From evolutionary biology, functional ecology, earth system modelling to landscape management, plant trait data are used to determine how the plants respond to the environmental factors and can act as indicators of ecosystem functions. In 2007, the TRY initiative was founded as an integrated database of trait data and all additional attributes relevant to understanding and interpreting a given trait value. Since then, the TRY database has integrated more than 400 datasets, including both original datasets and collective databases.</p><p>Due to the unique long table structure, the relevant information (e.g. trait names, species names, ancillary data representing context information, units of trait data, and identifiers for duplicates and outliers) for trait data filtering is stored at different places of the released TRY data. This makes the process to find all relevant information to select or remove trait data not straightforward without knowledge of the inherent data structure.</p><p>The &#8216;rtry&#8217; package is an R package that provides a set of easily applicable functions for the basic steps of data preprocessing and is designed in particular to support the data exploration and removal of the plant trait data, taking advantage of the features of trait data released from the TRY database. This package is supposed to be applicable without advanced knowledge of the data structure released from TRY or the R software. Most importantly, despite the &#8216;rtry&#8217; package being developed to support the application of plant trait data received via the TRY database, it is also applicable to other trait data.</p><p>&#160;</p>
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