Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

Abstract

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.hard to control using herbicides in fields, and its thorny leaves and high posture are a nuisance to grazing animals in pastures. It grows in dense strands and is more competitive than grass weeds, growing taller to overshadow adjacent species that compete for light [1].In recent years, unmanned aerial vehicles (UAVs) have proven to be useful in agricultural management. Their applications vary, but usually involve remote sensing, mapping, land modelling and precision agriculture. The most common use of UAVs in weed management is to acquire multispectral images in the visible and infrared spectrum for weed identification and mapping, however, a number of other potential uses have emerged, including the application of pesticides and other agrochemicals by the UAV itself [2]. Mapping weeds using UAVs provides high spatial resolutions, usually on the order of a few centimetres. This is vital in order to detect small weeds, weeds at a young stage, or within low crop cover with high background soil signal. In addition to the advantage of spatial resolution, UAVs offer temporal flexibility and can collect data at any time that fits the user's requirements, as compared to the satellite images. Moreover, UAVs can acquire images during overcast conditions, thus bypassing a frequent problem with satellite remote sensing, although cloud shadows and varying illumination conditions during image acquisition may affect data quality [3,4].A chall...

Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm

“…In the original methods, the input features include topographic openness, which represents the dominance (positive) or enclosure (negative) of a landscape (Yokoyama et al, 2002). We used spatial texture (Zisi et al, 2018), CHM, and VIs instead of openness, and different combinations were tested.…”

“…The layers of spatial texture (Texture) information were created by applying a local variance filter (7 × 7 pixels) to the RGB image (Zisi et al, 2018). Spatial texture describes visual effects caused by spatial variation in tonal quantity over relatively small areas (Anys & He, 1995).…”

“…Classification accuracy could be enhanced by combining the RF classifier with OBIA (De Castro et al, 2018). In addition, previous studies have reported that weed detection from UAV images could be improved by adding auxiliary information layers, such as spatial texture and vegetation height estimated from UAV digital surface models (DSMs) (Yuba et al, 2020b;Zisi et al, 2018).…”

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“…Classification accuracy could be enhanced by combining RF classifier with OBIA (de Castro et al., 2018). In addition, previous studies have reported that weed detection from UAV images could be improved using auxiliary information layers, such as spatial texture and vegetation height estimated from UAV digital surface models (DSM) (Zisi et al., 2018). From these earlier findings, we can expect improvement of weed detection accuracy by combining OBIA and RF classification using UAV’s color images and its auxiliary information.…”

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