Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery

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

doi:10.1016/j.compag.2017.05.026

Cited by 79 publications

(45 citation statements)

References 22 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%

“…The accuracy of mapping broadleaved weeds (Chenopodium album L. and Convolvulus arvensis L.) in a sunflower field in southern Spain ranged from 19% to 100%, with lower performances attributed to the use of a UAV camera capturing images in the visible wavelengths only [7]. Tamouridou et al [8] achieved accuracy of 87.04% and Kappa statistic of 74% using the maximum likelihood algorithm in multispectral images (green-red-near-infrared) along with the layer of texture, for the determination of S. marianum clusters in a field in which the dominant species was Avena sterilis L. Pantazi et al [9] used the Supervised Kohonen Network, Counter-Propagation Artificial Network and XY-Fusion Network on a multispectral UAV image (green-red-near-infrared) along with texture, for the determination of S. marianum clusters in the same field. The levels of accuracy achieved were high (>95% overall accuracy), partially attributed to the near-infrared band and texture layer.…”

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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%

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

et al. 2018

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“…This includes, for example, the development of new approaches for plant diseases identification on isolated plants species such as maize [17], apple [11], wheat [5], or potato [14] ; in addition to yield production [1] and crops quality evaluation [18]. As weed control has a major impact on agricultural production, several studies have been conducted to improve their detection, such as [15]. Nevertheless, the majority of these studies focus on a few crops or weed species (such as in [2]) and do not try to identify various weed species, in various agricultural systems.…”

Section: Related Workmentioning

confidence: 99%

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Mora-Fallas

Goëau

Joly

et al. 2020

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Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas, a gran escala.

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“…This study helps to accurate detection of fertilizers and fungicides according to the needs of the plant. In same year author [36] presented a system for weed detection which is based on counter propagation artificial neural network (CP-ANN) and as well as unmanned aircraft system for multispectral images captured for the identification of Silybum marianum which is difficult to eliminate and causes major loss on crop yield production. In this same year work also done for disease detection according to author [37] developed a system for the classification of parasites which was based on image processing procedure and the automatic identification of trips in strawberry fruit greenhouse environment, for control in real-time.…”

Section: Related Workmentioning

confidence: 99%

Predict Growth Stages of Wheat Crop using Digital Image Processing

Sharma*¹,

Yadav²

2020

IJRTE

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Manual examination is not as accurate to examine crop growing stages because of the possibility of the human mistake and errors. While machine examination or automatic examination can easily examine crop growing stages and increase productivity because it provides fast and accurate examine result. This study provide a solution to finding the wheat crop growth stages, Once the growing stages are established, farmers can take suitable and measured steps to improve the production of wheat or other agricultural crops. For finding the growth stages of wheat digital image processing technique is used. RGB model, HSI model, mean value of green colour, hue and saturation images use for examining wheat crop.

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Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery

Cited by 79 publications

References 22 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

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Predict Growth Stages of Wheat Crop using Digital Image Processing

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