Multi-spectral vision system for weed detection

Feyaerts, Filip; Gool, Luc Van

doi:10.1016/s0167-8655(01)00006-x

Cited by 69 publications

(41 citation statements)

References 9 publications

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“…The method for selecting the wavelengths was not mentioned. Feyaerts and Van Gool (2001) developed a spectrograph with a low spectral resolution (35 nm) and used it in field to discriminate beets from five weed species. The wavelength selection was carried out by examining weed-crop pairs to find the wavelengths that maximized a separation function.…”

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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“…In other words, it can be used for the processing of "offline" images obtained from representative areas of crops or fed into mechanical weed removal tools, providing input to be utilized in determining pulverization with a direct-injection sprayer. For Feyaerts and Gool (2001), systems of weed cover identification from images with accuracy rates of at least 80% can promote a reduction in the volume of herbicide applied in up to 90%. In the paper presented by Gerhards et al (2002), this reduction reached 98% when a prescription map and a pulverization mechanism with a direct-injection sprayer were used.…”

Section: Weed Infestation Level Based On Expert Evaluationmentioning

confidence: 99%

Development and testing of image processing algorithm to estimate weed infestation level in corn fields

Santiago¹,

Leite²,

Teruel³

et al. 2016

Aust J Crop Sci

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The challenges of modern agriculture have led to the development of localized management tools which allow the rationalization of the use and application of pesticides, a reduction in production costs and the optimization of agricultural processes. This study was carried out to develop an algorithm capable of orienting weed control in the management of a corn crop, using digital image analysis to identify the level of weed infestation in the field. The seeds of six species of weed were sown in an experimental plot of corn, and daily images were captured for 40 days for the evaluation of the level of weed infestation (low, intermediate or high). The algorithm developed was able to target information about the plants and soil accurately and discriminate the residual information as referring to either the culture or weeds. The proposed algorithm has achieved 90% accuracy in identifying the level of infestation from images already evaluated by experts. The results can thus be used as part of weed control strategy, with the incorporation of the geographic coordinates of the image making possible the construction of a map of the level of weed infestation in the different areas where the crop is growing.

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“… the spectral approach, in which the plant reflectance is the main parameter, using hyper-or multispectral images (Feyaerts & Van Gool, 2001;Vrindts, 2002;De Baerdemaeker et al, 2002). The difficulty is then to propose spectral differences sufficiently robusts to lightning conditions  the spatial approach, based on spatial criteria such as plant morphology (Chi et al, 2003;Manh et al, 2001), plant texture (Burks et al, 2000) … The main difficulty is tied here to the complexity and the natural variability of the scenes.…”

Section: Proxy-detection Image Processing For Weed-wheat Crop Discrimmentioning

confidence: 99%

Texture, Color and Frequential Proxy-Detection Image Processing for Crop Characterization in a Context of Precision Agriculture

Cointault¹,

Journaux²,

Rabatel³

et al. 2012

Agricultural Science

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Multi-spectral vision system for weed detection

Cited by 69 publications

References 9 publications

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

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

Development and testing of image processing algorithm to estimate weed infestation level in corn fields

Texture, Color and Frequential Proxy-Detection Image Processing for Crop Characterization in a Context of Precision Agriculture

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