2018
DOI: 10.1080/01431161.2017.1420940
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Combining image processing and machine learning to identify invasive plants in high-resolution images

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Cited by 32 publications
(24 citation statements)
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“…The longer battery life, the ability to plan automatic flights with easy-to-use ground station software, and their small size are real advantages, and the structure from motion algorithms (SFM) allow accurate digital elevation models (DEM) and ortho-mosaic terrain models over large areas. Today UAVs are increasingly accessible and have widespread applications, such as in environmental monitoring systems for agroforestry, structural geology, archaeology, marine habitats, supervised hazards, and accidents [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], and recently also in monitoring ML on the coast [40][41][42][43][44] or that floating in rivers [45]. These studies are not uniform with regard to the data processing procedures, ranging from visual interpretation of images [42] and analysis of the spectral profile of litter [46], to the use of machine learning methods [43,44].…”
Section: Introductionmentioning
confidence: 99%
“…The longer battery life, the ability to plan automatic flights with easy-to-use ground station software, and their small size are real advantages, and the structure from motion algorithms (SFM) allow accurate digital elevation models (DEM) and ortho-mosaic terrain models over large areas. Today UAVs are increasingly accessible and have widespread applications, such as in environmental monitoring systems for agroforestry, structural geology, archaeology, marine habitats, supervised hazards, and accidents [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], and recently also in monitoring ML on the coast [40][41][42][43][44] or that floating in rivers [45]. These studies are not uniform with regard to the data processing procedures, ranging from visual interpretation of images [42] and analysis of the spectral profile of litter [46], to the use of machine learning methods [43,44].…”
Section: Introductionmentioning
confidence: 99%
“…As such, it includes a broad range of algorithms, encompassing everything from simple linear regressions to deep learning-based neural networks [52]. The application of AI/ML for geomorphic error thresholding purposes is novel, with existing studies within freshwater settings applying it predominantly for classification of land cover types from image-derived parameters (e.g., [53][54][55][56]) or for the identification of other specific features of interest such as buildings (e.g., [57]) and invasive species (e.g., [58]). As such, our ultimate aim is to create the first high resolution, spatially continuous SfM-derived topographic change models in submerged fluvial environments constrained by spatially variable error estimates.…”
mentioning
confidence: 99%
“…Some systems are able to collect biological specimens for species analysis. Examples of invasive species detected through the use of drones in combination with image processing include: yellow flag iris (Iris pseudacorus; Baron et al 2018), invasive grasses (Cenchrus ciliaris and Triodia spp. ; Sandino et al 2018), Burmese pythons (Python bivittatus; Gomes 2017), and silk oak trees (Grevillea robusta; Strohecker 2017).…”
Section: Unmanned Aerial Vehicles and Remotely Operated Vehiclesmentioning
confidence: 99%