Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

Alexandridis, Thomas; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L.; Kashefi, J.; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

doi:10.3390/s17092007

Cited by 35 publications

(19 citation statements)

References 21 publications

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“…This study tested the use of vegetation canopy height together with multispectral UAV images in mapping mature S. marianum weeds. Relevant work has used plant height indirectly, with the incorporation of image texture for weed mapping with several classifiers [9,26] and object-based image analysis [27], as well as with the use of estimated plant height for segmentation of crops and weeds into objects but not directly in the classification algorithm [19]. However, none of the above-mentioned studies has evaluated the improvement of the multispectral image classification that was achieved by using the vegetation elevation information.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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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...

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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…In recent few years, machine learning methods have been successfully applied in weed mapping based of UAV imagery. Alexandridis et al [ 8 ] applied four novelty detection classifiers for weed detection and mapping of Silybum marianum (S. marianum) weed based on UAV multispectral imagery, and the identification accuracy using One Class Support Vector Machine (OC-SVM) reached an overall accuracy of 96%. Tamouridou et al [ 9 ] used the Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) to identify the S. marianum among other vegetation based on UAV remote sensing, and the S. marianum identification rate was up to 99.54%.…”

Section: Introductionmentioning

confidence: 99%

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Huang

Deng²,

Lan³

et al. 2018

PLoS ONE

140

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Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

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“…UAVs are able to fly at a low altitude [ 6 ], capturing imagery at a very high resolution [ 7 ], which is suitable for mapping weeds during their early growth stages. Though UAV remote sensing was proven to be effective in weed mapping tasks [ 8 , 9 ], the conversion of UAV imagery into accurate weed distribution maps is still the main bottleneck in SSWM applications.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have employed machine learning methods [ 8 , 9 ] for UAV imagery weed mapping tasks. Alexandridis et al [ 8 ] applied four machine learning approaches to map the distribution of S. marianum in a field.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery

Huang

Lan

Deng

et al. 2018

Sensors

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Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.

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Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

Cited by 35 publications

References 21 publications

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

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

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery

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