Weed Detection Using Color Machine Vision

El-Faki, M. S.; Zhang, N.; Peterson, Dallas E.

doi:10.13031/2013.3103

Cited by 63 publications

(27 citation statements)

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“…This parameter is influenced by the interaction between the species and the growth stage (Franz et al, 1991;El-Faki et al, 2000;Zhao et al, 2005), by stresses such as water or nitrogen deficiency (Goel et al, 2003;Zhao et al, 2005), by the angle between the leaf and the camera optical axis (Franz et al, 1991;Haralson et al, 1997cited in Noble et al, 2002 and by the substrate nature (Noble and Crowe, 2001;Noble et al, 2002).…”

Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

“…They may be attributed to particular conditions, such as crop regularly spaced in the field with no overlapping (Borregaard et al, 2000;Feyaerts and Van Gool, 2001), clearly different macrostructures of weeds and crops (Franz et al, 1991) or presence of a particular colour on certain plant stems (Zhang and Chaisattapagon, 1995;El-Faki et al, 2000).…”

Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

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Selection of the most efficient wavelength bands for discriminating weeds from crop

Piron

Leemans

Kleynen

et al. 2008

Computers and Electronics in Agriculture

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The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral device consisting of a black and white camera coupled with a rotating wheel holding 22 interference filters in the VIS-NIR domain. Measurements were performed over a period of 19 days, starting 1 week after crop emergence (early weeding can increase yields) and seven different weeds species were considered. The selection of the best filter combination was based on a quadratic discriminant analysis. The best combination of filters included three interference filters, respectively centred on 450, 550 and 700 nm. With this combination, the overall classification accuracy (CA) was 72%. When using only two filters, a slight degradation of the CA was noticed. When the classification results were reported on field images, a systematic misclassification of carrot cotyledons appears. Better results were obtained with a more advanced growth stage.

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Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

Selection of the most efficient wavelength bands for discriminating weeds from crop

Piron

Leemans

Kleynen

et al. 2008

Computers and Electronics in Agriculture

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“…Whereas, under a shade, the separation is increased. This is the reason that El-Faki et al (2000b) recommended imaging under dimmer illumination. Therefore, we also conclude that imaging under a shade should be preferred.…”

Section: Discussionmentioning

confidence: 99%

“…This is mainly important for imaging under a shade since the distance between the plant and the shade itself affects the shadow and its darkness due to which the illumination under a shade varies. El-Faki et al (2000b) argued that their plants at latter growth stages were classified better. This was due to the fact that the weed stems turned red (pigweed) as they grew.…”

Section: Discussionmentioning

confidence: 99%

Detecting creeping thistle in sugar beet fields using vegetation indices

Kazmi

García-Ruiz

Nielsen

et al. 2015

Computers and Electronics in Agriculture

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a b s t r a c tIn this article, we address the problem of thistle detection in sugar beet fields under natural, outdoor conditions. In our experiments, we used a commercial color camera and extracted vegetation indices from the images. A total of 474 field images of sugar beet and thistles were collected and divided into six different groups based on illumination, scale and age. The feature set was made up of 14 indices. Mahalanobis Distance (MD) and Linear Discriminant Analysis (LDA) were used to classify the species. Among the features, excess green (ExG), green minus blue (GB) and color index for vegetation extraction (CIVE) offered the highest average accuracy, above 90%. The feature set was reduced to four important indices following a PCA analysis, but the classification accuracy was similar to that obtained by only combining ExG and GB which was around 95%, still better than an individual index. Stepwise linear regression selected nine out of 14 features and offered the highest accuracy of 97%. The results of LDA and MD were fairly close, making them both equally preferable. Finally, the results were validated by annotating images containing both sugar beet and thistles using the trained classifiers. The validation experiments showed that sunlight followed by the size of the plant, which is related to its growth stage, are the two most important factors affecting the classification. In this study, the best results were achieved for images of young sugar beet (in the seventh week) under a shade.

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“…No contexto agrícola sua aplicação é grandemente explorada para identificação de pragas e mudas em contraste com solo, bem como para detecção de frutos devido às suas cores características. A facilidade de acesso a câmeras com aquisição de imagens coloridas, aliada ao fato destas fornecerem muito mais informações a respeito do ambiente do que as câmeras monocromáticas, e ainda a relativa facilidade da abordagem por cor em relação a abordagens de segmentação por forma e textura, tendem a favorecer a aplicação destas estratégias SLAUGHTER, 1998;EL-FAKI;ZHANG;PETERSON, 2000;HAMUDA;GLAVIN;JONES, 2016).…”

Section: "Sistema De Gerenciamento Agrícola Baseado Na Variação Espacunclassified

Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas

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Weed Detection Using Color Machine Vision

Cited by 63 publications

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Selection of the most efficient wavelength bands for discriminating weeds from crop

Selection of the most efficient wavelength bands for discriminating weeds from crop

Detecting creeping thistle in sugar beet fields using vegetation indices

Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas

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