Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery

Tamouridou, Alexandra A.; Alexandridis, Thomas; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L.; Kashefi, J.; Kasampalis, Dimitrios A.; Kontouris, Giorgos; Moshou, Dimitrios

doi:10.3390/s17102307

Cited by 28 publications

(27 citation statements)

References 16 publications

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“…The positive influence of the texture layer is due to its wide contrast between the S. marianum patches with a very high texture due to the interchange between the tall plants and the gaps between them, and on the other hand the relatively low and uniform other types of vegetation that are characterised by very low texture. A positive influence was also noted by the inclusion of a texture layer together with UAV bands, when evaluating the weights of features in the hidden neurons of an artificial neural network (multilayer perceptron with automatic relevance determination) that was tested for weed mapping in the study area [28].…”

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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“…ANNs are used in various scientific fields including, for example, bioinformatics, biochemistry, medicine, meteorology, economic sciences, robotics, aquaculture, food security and climatology. ANNs are also used in agriculture, agrophysics or agricultural engineering [1,35,[40][41][42][43]47].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Francik

Kurpaska

2020

Sensors

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It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

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“…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

133

101

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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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Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery

Cited by 28 publications

References 16 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

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

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

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